CN119206799A - Anesthesia cabinet application method based on fingerprint recognition and anesthesia cabinet - Google Patents
Anesthesia cabinet application method based on fingerprint recognition and anesthesia cabinet Download PDFInfo
- Publication number
- CN119206799A CN119206799A CN202411239606.0A CN202411239606A CN119206799A CN 119206799 A CN119206799 A CN 119206799A CN 202411239606 A CN202411239606 A CN 202411239606A CN 119206799 A CN119206799 A CN 119206799A
- Authority
- CN
- China
- Prior art keywords
- characteristic
- fingerprint
- ridge
- valley
- grid
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 206010002091 Anaesthesia Diseases 0.000 title claims abstract description 63
- 230000037005 anaesthesia Effects 0.000 title claims abstract description 63
- 238000000034 method Methods 0.000 title claims abstract description 46
- 238000005259 measurement Methods 0.000 claims abstract description 40
- 230000003287 optical effect Effects 0.000 claims abstract description 23
- 238000004458 analytical method Methods 0.000 claims description 15
- 230000008569 process Effects 0.000 claims description 9
- 238000012545 processing Methods 0.000 claims description 9
- 238000003708 edge detection Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000010586 diagram Methods 0.000 claims description 4
- 210000004243 sweat Anatomy 0.000 abstract description 18
- 238000003384 imaging method Methods 0.000 abstract description 10
- 239000003640 drug residue Substances 0.000 abstract description 4
- 238000005516 engineering process Methods 0.000 abstract description 2
- 239000003814 drug Substances 0.000 description 9
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 8
- 230000008859 change Effects 0.000 description 6
- 230000006870 function Effects 0.000 description 5
- 230000007246 mechanism Effects 0.000 description 4
- 238000012795 verification Methods 0.000 description 3
- 206010013786 Dry skin Diseases 0.000 description 2
- 238000003491 array Methods 0.000 description 2
- 230000037336 dry skin Effects 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000002310 reflectometry Methods 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 239000010409 thin film Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B50/00—Containers, covers, furniture or holders specially adapted for surgical or diagnostic appliances or instruments, e.g. sterile covers
- A61B50/10—Furniture specially adapted for surgical or diagnostic appliances or instruments
- A61B50/18—Cupboards; Drawers therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
- G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
- G06V10/757—Matching configurations of points or features
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
- G06V40/13—Sensors therefor
- G06V40/1318—Sensors therefor using electro-optical elements or layers, e.g. electroluminescent sensing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
- G06V40/1347—Preprocessing; Feature extraction
- G06V40/1359—Extracting features related to ridge properties; Determining the fingerprint type, e.g. whorl or loop
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
- G06V40/1365—Matching; Classification
- G06V40/1376—Matching features related to ridge properties or fingerprint texture
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Biomedical Technology (AREA)
- Human Computer Interaction (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Public Health (AREA)
- Surgery (AREA)
- Evolutionary Computation (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Databases & Information Systems (AREA)
- Computing Systems (AREA)
- Artificial Intelligence (AREA)
- Primary Health Care (AREA)
- Epidemiology (AREA)
- Software Systems (AREA)
- General Business, Economics & Management (AREA)
- Business, Economics & Management (AREA)
- Heart & Thoracic Surgery (AREA)
- Molecular Biology (AREA)
- Animal Behavior & Ethology (AREA)
- Veterinary Medicine (AREA)
- Collating Specific Patterns (AREA)
Abstract
本发明属于指纹识别技术领域，提出了一种基于指纹识别的麻醉柜应用方法及麻醉柜，该方法包括以下步骤：通过光学式指纹传感器获取指纹图片；对指纹图片进行灰度化获取指纹灰度图；根据指纹灰度图获取距离特征线；通过距离特征线获取脊谷特征距离度量值和脊谷特征异动量，并将脊谷特征距离度量值和脊谷特征异动量进行储存；在需要打开麻醉柜时，获取待识别用户的脊谷特征距离度量值和脊谷特征异动量，并根据储存的脊谷特征距离度量值和脊谷特征异动量判断是否打开麻醉柜。根据本发明实施例的识别方法，即使医护人员手上有汗水、手套印记、药物残留等影响指纹成像的因素，也能准确识别医护人员的指纹特征是否与储存的指纹特征一致。
The present invention belongs to the field of fingerprint recognition technology, and proposes an anesthesia cabinet application method and anesthesia cabinet based on fingerprint recognition, the method comprising the following steps: obtaining a fingerprint image through an optical fingerprint sensor; graying the fingerprint image to obtain a fingerprint grayscale image; obtaining a distance feature line according to the fingerprint grayscale image; obtaining a ridge-valley feature distance measurement value and a ridge-valley feature anomaly through the distance feature line, and storing the ridge-valley feature distance measurement value and the ridge-valley feature anomaly; when the anesthesia cabinet needs to be opened, obtaining the ridge-valley feature distance measurement value and the ridge-valley feature anomaly of the user to be identified, and judging whether to open the anesthesia cabinet according to the stored ridge-valley feature distance measurement value and the ridge-valley feature anomaly. According to the recognition method of the embodiment of the present invention, even if there are factors that affect fingerprint imaging such as sweat, glove marks, and drug residues on the hands of medical staff, it is possible to accurately identify whether the fingerprint characteristics of the medical staff are consistent with the stored fingerprint characteristics.
Description
Technical Field
The invention belongs to the technical field of fingerprint identification, and particularly relates to an anesthesia cabinet application method based on fingerprint identification and an anesthesia cabinet.
Background
At present, in medical environments, particularly in operating rooms or emergency situations, the quality of fingerprint images is often reduced due to hand perspiration, glove marks, drug residues and the like, so that the fingerprint images are difficult to accurately identify. Traditional fingerprint identification technologies mainly rely on integral image matching or absolute matching of minutiae (such as end points and bifurcation points), and the methods require high definition and consistency of fingerprint images, and slight deviation can cause identification failure. However, in a medical scenario, since the finger surface is often affected by various uncontrollable factors, conventional techniques often cannot accommodate these changes, resulting in a significant reduction in recognition success rate, affecting quick and accurate authentication of medical personnel in an emergency, and possibly delaying critical medical operations. Therefore, the medical environment has higher requirements on fingerprint identification, and needs to have stronger fault tolerance capability and robustness so as to cope with complex fingerprint changes and ensure that efficient and reliable identity identification can be realized under various conditions.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems in the related art to some extent. Therefore, a first object of the present invention is to provide an application method of an anesthesia cabinet based on fingerprint identification, which can accurately identify whether fingerprint features of medical staff are consistent with stored fingerprint features even if sweat, glove marks, medicine residues and other factors affecting fingerprint imaging exist on the hands of the medical staff in an operating room or emergency;
A second object of the invention is to propose an anesthesia cabinet.
To achieve the above object, an embodiment of a first aspect of the present invention provides a fingerprint identification-based anesthesia cabinet application method, which includes the following steps:
S100, acquiring a fingerprint picture through an optical fingerprint sensor;
S200, graying the fingerprint picture to obtain a fingerprint gray-scale picture;
s300, acquiring a distance characteristic line according to the fingerprint gray level diagram;
s400, obtaining a ridge-valley characteristic distance measurement value and a ridge-valley characteristic difference value through the distance characteristic line, and storing the ridge-valley characteristic distance measurement value and the ridge-valley characteristic difference value.
S500, when the anesthesia cabinet needs to be opened, acquiring a ridge-valley characteristic distance measurement value and ridge-valley characteristic difference quantity of a user to be identified, and judging whether to open the anesthesia cabinet according to the stored ridge-valley characteristic distance measurement value and ridge-valley characteristic difference quantity.
According to the identification method provided by the embodiment of the invention, the accurate identification of the fingerprint can be realized.
Further, the step S100 of acquiring the fingerprint image by the optical fingerprint sensor comprises the step of using the optical fingerprint sensor, wherein the optical fingerprint sensor comprises an optical scanning module and an image sensor, the optical sensor captures the fingerprint image by projecting light onto the fingerprint and detecting reflected light, the user places the finger on the sensor, and the sensor acquires the fingerprint image by an optical method to generate the original fingerprint image.
In step S200, the process of graying the fingerprint image to obtain a fingerprint gray image includes:
The fingerprint image is subjected to graying processing, the fingerprint image after the graying processing is recorded as a first gray image, edges in the image are detected through a Canny edge detection algorithm, ridge lines and valley lines of the fingerprint are highlighted, connected areas in the gray image are identified and filled to determine main characteristic areas of the fingerprint, the process of identifying and filling the connected areas in the gray image comprises the steps of scanning the image line by line, finding out each connected area, and distributing a unique label for each connected area, wherein the connected areas are areas with the same pixel value in the gray image. Highlighting the connected regions by filling all pixels within the regions to a specific value (e.g. black or white), wherein the region filling may comprise a flood fill algorithm starting from an initial point and recursively filling adjacent pixels to the region boundaries, and after edge detection and region filling processing, obtaining a set of feature regions comprising fingerprint ridges and valleys representing key details in the fingerprint image, facilitating subsequent fingerprint analysis and matching.
In a medical environment, particularly in an operating room or an emergency, fingerprints may be difficult to accurately identify due to sweat, glove marks, drug residues and the like on hands of medical staff, whereas traditional fingerprint identification generally depends on integral image matching or absolute matching of simple detail characteristic points (such as end points and bifurcation points), and the absolute matching can greatly reduce the success rate of identification of the medical staff in the medical environment and cannot obtain correct fingerprint identification.
In step S300, a distance feature line is obtained according to the fingerprint gray-scale image;
S301, using zj (j) to represent the gray value of the grid of the characteristic region of the jth fingerprint ridge, wherein the value range of j is [1, K ], wherein K is the number of the grids of the characteristic region of the fingerprint ridge, zg (g) represents the gray value of the grid of the characteristic region of the jth fingerprint valley, g is [1, H ], wherein H is the number of the grids of the characteristic region of the fingerprint valley, the gray value of the grid is the average value of the gray values of all pixels in the grid, the grid of the characteristic region of the fingerprint ridge with the maximum gray value of the grid is taken as zjxu, the gray value of zjxu is taken as zjxp, and the grid of the characteristic region of the fingerprint valley with the maximum gray value of the grid is taken as zgxu, and the gray value of zgxu is taken as zgxp;
S302, defining an integer variable k, setting an initial value to 1, creating two variables HDzh and HDzh2 that are initially zero for subsequent calculation and comparison.
S303, gray level characteristic analysis of gray level values is carried out on the grids of the characteristic areas of the fingerprint ridge lines and the grids of the characteristic areas of the fingerprint valley lines, and two blank sequences HDa and HDb are created.
S304, calculating HDzh and HDzh2 values, wherein HDzh is calculated as zjxp-zj (k), HDzh is calculated as zgxp-zg (k), and comparing HDzh and HDzh values, wherein if HDzh1 is larger than HDzh2, the grid sequence number where zj (j) is located is added to the sequence HDa. If HDzh1 is less than or equal to HDzh2, then the grid number at zg (g) is added to sequence HDb.
S305, judging whether the gray feature analysis is finished, if the current variable K is smaller than K or smaller than H, increasing K by 1, returning to the step S303, continuing the gray feature analysis, and if the current variable j is not smaller than K or not smaller than H, indicating that the gray feature analysis is finished.
S306, acquiring the gray value under the grid corresponding to the grid serial number in the sequence HDa as the median of the gray values under the grids corresponding to all the grid serial numbers in the sequence HDa, marking the grid as zjx, acquiring the gray value under the grid corresponding to the grid serial number in the sequence HDb as the median of the gray values under the grids corresponding to all the grid serial numbers in the sequence HDb, and marking the grid as zgx;
The method has the advantages that a thin film is formed on the surface of skin due to sweat, so that the shape of a finger expands when pressed, the ridge and the valley in a fingerprint image expand or shrink to be blurred, and because the water has higher reflectivity, compared with dry skin, more light rays are reflected back to a camera or a sensor, the gray values of the areas are much higher than the actual gray values, when the finger is stained, the water covers the surface of the finger, the optical characteristics of the fingerprint are changed, the part with the water reflects more light rays, the gray values of the stained part become larger in the area with brighter gray images, the mesh number irrelevant to the mesh with the maximum gray value in the characteristic area of the ridge and the valley of the fingerprint can be obtained through screening HDa sequences and HDb sequences, the meshes represent obvious textures in the fingerprint image and areas not obscured by the sweat, the fingerprint can be well identified, the factors such as the water in the fingerprint image, the noise and the noise caused by the sweat can be avoided, the medical system has the reliability and the reliability of the environment is improved, and the reliability of the fingerprint is greatly improved, and the reliability is greatly influenced by the factors such as the system is greatly influenced by the factors such as the noise and the noise.
S307, obtaining the distance between grids of the characteristic areas of all fingerprint ridges, wherein the distance between grids is the length of a line segment, which is connected with a grid center point, of the grid of the characteristic area of one fingerprint ridge and the grid of the characteristic area of all other fingerprint ridges, and the shortest distance between the grid of the characteristic area of the j fingerprint ridge and the grid of the other fingerprint ridge is recorded as the shortest adjacent distance;
Specifically, ridge lines and valley lines are main structural features in the fingerprint, the gray values of the ridge lines and the valley lines can reflect local details and texture changes of the fingerprint, and a ridge line feature area and a valley line feature area with the maximum gray values are found by comparing gray values of different grids, and the areas are key feature points in the fingerprint and are the basis for analyzing the fingerprint.
Further, the shortest neighbor distance can reflect the density and the relative position of the ridge lines, and is used for understanding the overall structure of the fingerprint.
S308, obtaining a distance characteristic line, wherein the distance characteristic line comprises a first distance characteristic line, a second distance characteristic line and a third distance characteristic line, and specifically, the center point of a line segment between a center point of zgx and a center point of zjx is recorded as P4, the center point of a distance special grid zjt is recorded as P5, the center point of a grid of a distance special adjacent grid zjp is recorded as P6, the center point of the line segment between the points P4 and P5 is recorded as P6, the line segment between the points P3 and P6 is a first distance characteristic line L1, the line segment between the points P3 and P4 is a second distance characteristic line L2, and the line segment between the points P3 and P5 is a third distance characteristic line L3;
The first distance characteristic line L1 provides a relative position relation between ridge lines and valley lines, the second characteristic line L2 and the third distance characteristic line L3 describe distances between important structural characteristics and other characteristic points in the fingerprint, the distance characteristic lines are used for fingerprint matching and identity verification in subsequent steps, and the similarity and difference between different fingerprints can be compared by comparing the lengths and the relative positions of the characteristic lines, so that the accuracy of fingerprint identification is enhanced.
The method has the beneficial effects that key geometric features in the fingerprint gray level diagram are calculated and analyzed, the distance feature lines of ridge lines and valley lines are extracted, the feature lines describe the details and the structure of the fingerprint, an important basis is provided for accurate matching and identity verification of the fingerprint, the robustness and the accuracy of a fingerprint identification system are enhanced through the process, and efficient identity verification is facilitated.
S400, obtaining a ridge-valley characteristic distance measurement value and a ridge-valley characteristic difference value through a distance characteristic line, and storing the ridge-valley characteristic distance measurement value and the ridge-valley characteristic difference value;
Calculating a ridge-valley characteristic difference value ld, wherein ld=abs (lka-lkb), wherein abs is an absolute value function, namely the ridge-valley characteristic difference value ld is an absolute value of a direct difference value between the first characteristic length lka and the second characteristic length lkb;
Specifically, by calculating the length of the distance feature line, the geometric features of the fingerprint can be accurately quantified. The ridge valley feature distance metric lg, the first feature length lka, the second feature length lkb provide a specific spatial relationship of ridge lines and valley lines in the fingerprint, where ridge valley feature difference ld is the absolute difference between the first feature length lka and the second feature length lkb, which can help capture fine structural differences in the fingerprint, enabling the system to identify very fine fingerprint features,
Further, a certain variation range can be allowed in the fingerprint identification process by calculating the ridge-valley characteristic difference ld. Since the actual fingerprint may have small variations under different conditions (such as finger position, pressure, etc.), the system can still accurately identify the actual fingerprint within the variation range by calculating the variation of the ridge-valley characteristics.
Further, the ridge-valley characteristic distance measurement value lg and the ridge-valley characteristic difference value ld are stored.
S500, when the anesthesia cabinet needs to be opened, acquiring a ridge-valley characteristic distance measurement value and ridge-valley characteristic difference quantity of a user to be identified, and judging whether to open the anesthesia cabinet according to the stored ridge-valley characteristic distance measurement value and ridge-valley characteristic difference quantity;
The user to be identified is a user who needs to open the anesthesia cabinet;
And if the ridge-valley characteristic distance measurement value of the user to be identified is not satisfied with the ridge-valley characteristic distance measurement value of the user to be identified and the stored ridge-valley characteristic distance measurement value lg are equal, and the ridge-valley characteristic difference value ld of the user to be identified are both equal, the anesthesia cabinet is not opened, and the identification failure is displayed.
Because medical staff has sweat, glove marks, medicine residues and other factors that influence fingerprint imaging on hand, certain errors can exist in the ridge-valley characteristic different quantity and the ridge-valley characteristic different quantity obtained under medical environment, in order to avoid the errors, the dislocation interval difference needs to be further calculated to avoid recognition failure caused by small deviation.
In practical application, the ridge line and valley line of the fingerprint may change slightly due to the angle, pressure or environmental factors of the finger, or the medical staff has sweat, glove marks, medicine residues and other factors affecting fingerprint imaging in the medical environment, and the tolerance of the system to the changes can be improved by allowing the characteristics of the fingerprint to change within a certain range through the dislocation interval difference, so that the robustness and accuracy of the fingerprint identification system are enhanced.
Preferably, when the ridge-valley characteristic distance metric value of the user to be identified is equal to the stored ridge-valley characteristic distance metric value lg and the ridge-valley characteristic different quantity ld of the user to be identified are both equal, the anesthesia cabinet is opened and the identification is successfully displayed, when the ridge-valley characteristic distance metric value and the ridge-valley characteristic different quantity of the user to be identified are not met, the ridge-valley characteristic distance metric value of the user to be identified is equal to the stored ridge-valley characteristic distance metric value lg and the ridge-valley characteristic different quantity ld of the user to be identified are both equal, the absolute value of the difference value between the ridge-valley characteristic distance metric value and the ridge-valley characteristic distance metric value lg of the user to be identified is calculated and recorded as the dislocation interval difference, the anesthesia cabinet is opened and the identification is successfully displayed if the dislocation interval difference is smaller than the ridge-valley characteristic different quantity ld, the anesthesia cabinet is not opened and the identification is failed is displayed.
Because when the ridge-valley characteristic distance measurement value and the ridge-valley characteristic difference ld of the user are not completely matched, whether the anesthesia cabinet is allowed to be opened or not can be further judged by calculating the dislocation interval difference, the mechanism improves the fault tolerance of the system, and recognition failure caused by tiny deviation is avoided.
The method has the advantages that absolute matching is not relied on, a concept of dislocation interval difference is introduced, certain deviation is allowed during identification, so long as the deviation is within a set fault tolerance range, the matching can still be considered as successful, the fault tolerance mechanism is particularly suitable for slight identification deviation of fingers possibly caused by angle, pressure or surface state change, the success rate of identification is improved, and even if sweat, glove marks, medicine residues and other factors affecting fingerprint imaging exist on hands of medical staff in an operating room or emergency, whether fingerprint features of the medical staff are consistent with stored fingerprint features can be accurately identified.
In order to achieve the above object, the second embodiment of the present invention further provides an anesthesia cabinet, which is characterized in that the anesthesia cabinet is identified and opened by an anesthesia cabinet application method based on fingerprint identification, and the anesthesia cabinet can accurately identify whether the fingerprint characteristics of the medical staff are consistent with the stored fingerprint characteristics even if the medical staff has factors affecting fingerprint imaging such as sweat, glove marks, medicine residues and the like on the hands of the medical staff in an operating room or an emergency.
Drawings
Fig. 1 is a flowchart of an application method of an anesthesia cabinet based on fingerprint identification.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
Fig. 1 is a flowchart of an application method of an anesthesia cabinet based on fingerprint identification.
Referring to fig. 1, the invention provides an application method of an anesthesia cabinet based on fingerprint identification, which comprises the following steps:
S100, acquiring a fingerprint picture through an optical fingerprint sensor;
S200, graying the fingerprint picture to obtain a fingerprint gray-scale picture;
s300, acquiring a distance characteristic line according to the fingerprint gray level diagram;
s400, obtaining a ridge-valley characteristic distance measurement value and a ridge-valley characteristic difference value through the distance characteristic line, and storing the ridge-valley characteristic distance measurement value and the ridge-valley characteristic difference value.
S500, when the anesthesia cabinet needs to be opened, acquiring a ridge-valley characteristic distance measurement value and ridge-valley characteristic difference quantity of a user to be identified, and judging whether to open the anesthesia cabinet according to the stored ridge-valley characteristic distance measurement value and ridge-valley characteristic difference quantity.
According to the identification method provided by the embodiment of the invention, the accurate identification of the fingerprint can be realized.
Further, the step S100 of acquiring the fingerprint image by the optical fingerprint sensor comprises the step of using the optical fingerprint sensor, wherein the optical fingerprint sensor comprises an optical scanning module and an image sensor, the optical sensor captures the fingerprint image by projecting light onto the fingerprint and detecting reflected light, the user places the finger on the sensor, and the sensor acquires the fingerprint image by an optical method to generate the original fingerprint image.
In step S200, the process of graying the fingerprint image to obtain a fingerprint gray image includes:
The fingerprint picture is subdivided 8 to 32 times. After subdivision, the fingerprint picture is divided into G sub-areas, kj (i) represents the ith sub-area, the value range of i is [1, G ], and G is the number of sub-areas.
The fingerprint image is subjected to graying processing, the fingerprint image after the graying processing is recorded as a first gray image, edges in the image are detected through a Canny edge detection algorithm, ridge lines and valley lines of the fingerprint are highlighted, connected areas in the gray image are identified and filled to determine main characteristic areas of the fingerprint, the process of identifying and filling the connected areas in the gray image comprises the steps of scanning the image line by line, finding out each connected area, and distributing a unique label for each connected area, wherein the connected areas are areas with the same pixel value in the gray image. Highlighting the connected regions by filling all pixels within the regions to a specific value (e.g. black or white), wherein the region filling may be done by a flood fill algorithm, starting from an initial point, recursively filling adjacent pixels until the region boundaries. After edge detection and region filling processing, a characteristic region set containing fingerprint ridges and valleys is obtained, and the characteristic regions of the fingerprint ridges and the valleys represent key details in a fingerprint image, so that subsequent fingerprint analysis and matching are facilitated.
In a medical environment, particularly in an operating room or an emergency, fingerprints may be difficult to accurately identify due to sweat, glove marks, drug residues and the like on hands of medical staff, whereas traditional fingerprint identification generally depends on integral image matching or absolute matching of simple detail characteristic points (such as end points and bifurcation points), and the absolute matching can greatly reduce the success rate of identification of the medical staff in the medical environment and cannot obtain correct fingerprint identification.
In step S300, a distance feature line is obtained according to the fingerprint gray-scale image;
S301, using zj (j) to represent the gray value of the grid of the characteristic region of the jth fingerprint ridge, wherein the value range of j is [1, K ], wherein K is the number of the grids of the characteristic region of the fingerprint ridge, zg (g) represents the gray value of the grid of the characteristic region of the jth fingerprint valley, g is [1, H ], wherein H is the number of the grids of the characteristic region of the fingerprint valley, the gray value of the grid is the average value of the gray values of all pixels in the grid, the grid of the characteristic region of the fingerprint ridge with the maximum gray value of the grid is taken as zjxu, the gray value of zjxu is taken as zjxp, and the grid of the characteristic region of the fingerprint valley with the maximum gray value of the grid is taken as zgxu, and the gray value of zgxu is taken as zgxp;
S302, defining an integer variable k, setting an initial value to 1, creating two variables HDzh and HDzh2 that are initially zero for subsequent calculation and comparison.
S303, gray level characteristic analysis of gray level values is carried out on the grids of the characteristic areas of the fingerprint ridge lines and the grids of the characteristic areas of the fingerprint valley lines, and two blank sequences HDa and HDb are created.
S304, calculating HDzh and HDzh2 values, wherein HDzh is calculated as zjxp-zj (k), HDzh is calculated as zgxp-zg (k), and comparing HDzh and HDzh values, wherein if HDzh1 is larger than HDzh2, the grid sequence number where zj (j) is located is added to the sequence HDa. If HDzh1 is less than or equal to HDzh2, then the grid number at zg (g) is added to sequence HDb.
S305, judging whether the gray feature analysis is finished, if the current variable K is smaller than K or smaller than H, increasing K by 1, returning to the step S303, continuing the gray feature analysis, and if the current variable j is not smaller than K or not smaller than H, indicating that the gray feature analysis is finished.
S306, acquiring the gray value under the grid corresponding to the grid serial number in the sequence HDa as the median of the gray values under the grids corresponding to all the grid serial numbers in the sequence HDa, marking the grid as zjx, acquiring the gray value under the grid corresponding to the grid serial number in the sequence HDb as the median of the gray values under the grids corresponding to all the grid serial numbers in the sequence HDb, and marking the grid as zgx;
The method has the advantages that a thin film is formed on the surface of skin due to sweat, so that the shape of a finger expands when pressed, the ridge and the valley in a fingerprint image expand or shrink to be blurred, and because the water has higher reflectivity, compared with dry skin, more light rays are reflected back to a camera or a sensor, the gray values of the areas are much higher than the actual gray values, when the finger is stained, the water covers the surface of the finger, the optical characteristics of the fingerprint are changed, the part with the water reflects more light rays, the gray values of the stained part become larger in the area with brighter gray images, the mesh number irrelevant to the mesh with the maximum gray value in the characteristic area of the ridge and the valley of the fingerprint can be obtained through screening HDa sequences and HDb sequences, the meshes represent obvious textures in the fingerprint image and areas not obscured by the sweat, the fingerprint can be well identified, the factors such as the water in the fingerprint image, the noise and the noise caused by the sweat can be avoided, the medical system has the reliability and the reliability of the environment is improved, and the reliability of the fingerprint is greatly improved, and the reliability is greatly influenced by the factors such as the system is greatly influenced by the factors such as the noise and the noise.
S307, obtaining the distance between grids of the characteristic areas of all fingerprint ridges, wherein the distance between grids is the length of a line segment, which is connected with a grid center point, of the grid of the characteristic area of one fingerprint ridge and the grid of the characteristic area of all other fingerprint ridges, and the shortest distance between the grid of the characteristic area of the j fingerprint ridge and the grid of the other fingerprint ridge is recorded as the shortest adjacent distance;
Specifically, ridge lines and valley lines are main structural features in the fingerprint, the gray values of the ridge lines and the valley lines can reflect local details and texture changes of the fingerprint, and a ridge line feature area and a valley line feature area with the maximum gray values are found by comparing gray values of different grids, and the areas are key feature points in the fingerprint and are the basis for analyzing the fingerprint.
Further, the shortest neighbor distance can reflect the density and the relative position of the ridge lines, and is used for understanding the overall structure of the fingerprint.
S308, obtaining a distance characteristic line, wherein the distance characteristic line comprises a first distance characteristic line, a second distance characteristic line and a third distance characteristic line, specifically, the center point of a line segment between the center point of zgx and the center point of zjx is recorded as P3, the center point of the distance special grid zjt is recorded as P4, the grid center point of the distance special adjacent grid zjp is recorded as P5, the center point of the line segment between the recorded points P4 and P5 is recorded as P6, the line segment between the recorded points P3 and P6 is a first distance characteristic line L1, the line segment between the recorded points P3 and P4 is a second distance characteristic line L2, and the line segment between the recorded points P3 and P5 is a third distance characteristic line L3.
S400, obtaining a ridge-valley characteristic distance measurement value and a ridge-valley characteristic difference value through a distance characteristic line, and storing the ridge-valley characteristic distance measurement value and the ridge-valley characteristic difference value;
Calculating a ridge-valley characteristic difference value ld, wherein ld=abs (lka-lkb), wherein abs is an absolute value function, namely the ridge-valley characteristic difference value ld is an absolute value of a direct difference value between the first characteristic length lka and the second characteristic length lkb;
Specifically, by calculating the length of the distance feature line, the geometric features of the fingerprint can be accurately quantified. The ridge valley feature distance metric lg, the first feature length lka, the second feature length lkb provide a specific spatial relationship of ridge lines and valley lines in the fingerprint, where ridge valley feature difference ld is the absolute difference between the first feature length lka and the second feature length lkb, which can help capture fine structural differences in the fingerprint, enabling the system to identify very fine fingerprint features,
Further, a certain variation range can be allowed in the fingerprint identification process by calculating the ridge-valley characteristic difference ld. Since the actual fingerprint may have small variations under different conditions (such as finger position, pressure, etc.), the system can still accurately identify the actual fingerprint within the variation range by calculating the variation of the ridge-valley characteristics.
Further, the ridge-valley characteristic distance measurement value lg and the ridge-valley characteristic difference value ld are stored.
S500, when the anesthesia cabinet needs to be opened, acquiring a ridge-valley characteristic distance measurement value and ridge-valley characteristic difference quantity of a user to be identified, and judging whether to open the anesthesia cabinet according to the stored ridge-valley characteristic distance measurement value and ridge-valley characteristic difference quantity;
The user to be identified is a user who needs to open the anesthesia cabinet;
And if the ridge-valley characteristic distance measurement value of the user to be identified is not satisfied with the ridge-valley characteristic distance measurement value of the user to be identified and the stored ridge-valley characteristic distance measurement value lg are equal, and the ridge-valley characteristic difference value ld of the user to be identified are both equal, the anesthesia cabinet is not opened, and the identification failure is displayed.
Because medical staff has sweat, glove marks, medicine residues and other factors that influence fingerprint imaging on hand, certain errors can exist in the ridge-valley characteristic different quantity and the ridge-valley characteristic different quantity obtained under medical environment, in order to avoid the errors, the dislocation interval difference needs to be further calculated to avoid recognition failure caused by small deviation.
In practical application, the ridge line and valley line of the fingerprint may change slightly due to the angle, pressure or environmental factors of the finger, or the medical staff has sweat, glove marks, medicine residues and other factors affecting fingerprint imaging in the medical environment, and the tolerance of the system to the changes can be improved by allowing the characteristics of the fingerprint to change within a certain range through the dislocation interval difference, so that the robustness and accuracy of the fingerprint identification system are enhanced.
Preferably, when the ridge-valley characteristic distance metric value of the user to be identified is equal to the stored ridge-valley characteristic distance metric value lg and the ridge-valley characteristic different quantity ld of the user to be identified are both equal, the anesthesia cabinet is opened and the identification is successfully displayed, when the ridge-valley characteristic distance metric value and the ridge-valley characteristic different quantity of the user to be identified are not met, the ridge-valley characteristic distance metric value of the user to be identified is equal to the stored ridge-valley characteristic distance metric value lg and the ridge-valley characteristic different quantity ld of the user to be identified are both equal, the absolute value of the difference value between the ridge-valley characteristic distance metric value and the ridge-valley characteristic distance metric value lg of the user to be identified is calculated and recorded as the dislocation interval difference, the anesthesia cabinet is opened and the identification is successfully displayed if the dislocation interval difference is smaller than the ridge-valley characteristic different quantity ld, the anesthesia cabinet is not opened and the identification is failed is displayed.
When the ridge-valley characteristic distance measurement value and the ridge-valley characteristic difference ld of the user are not completely matched, whether the anesthesia cabinet is allowed to be opened or not can be further judged by calculating the dislocation interval difference, and the mechanism improves the fault tolerance of the system and avoids recognition failure caused by small deviation.
The method has the advantages that absolute matching is not relied on, a concept of dislocation interval difference is introduced, certain deviation is allowed during identification, so long as the deviation is within a set fault tolerance range, the matching can still be considered as successful, the fault tolerance mechanism is particularly suitable for slight identification deviation of fingers possibly caused by angle, pressure or surface state change, the success rate of identification is improved, and even if sweat, glove marks, medicine residues and other factors affecting fingerprint imaging exist on hands of medical staff in an operating room or emergency, whether fingerprint features of the medical staff are consistent with stored fingerprint features can be accurately identified.
The invention also provides an anesthesia cabinet which is characterized in that the anesthesia cabinet is identified and started by an application method of the anesthesia cabinet based on fingerprint identification, and the anesthesia cabinet can accurately identify whether fingerprint characteristics of medical staff are consistent with stored fingerprint characteristics or not even if sweat, glove marks, medicine residues and other factors affecting fingerprint imaging exist on hands of the medical staff in an operating room or an emergency.
It should be noted that the logic and/or steps represented in the flowcharts or otherwise described herein, for example, may be considered as a ordered listing of executable instructions for implementing logical functions, and may be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include an electrical connection (an electronic device) having one or more wires, a portable computer diskette (a magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of techniques known in the art, discrete logic circuits with logic gates for implementing logic functions on data signals, application specific integrated circuits with appropriate combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," and the like, as used in embodiments of the present invention, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or as implying any particular number of features in the present embodiment. Thus, a feature of an embodiment of the invention that is defined by terms such as "first," "second," etc., may explicitly or implicitly indicate that at least one such feature is included in the embodiment. In the description of the present invention, the word "plurality" means at least two or more, for example, two, three, four, etc., unless explicitly defined otherwise in the embodiments.
In the present invention, unless explicitly stated or limited otherwise in the examples, the terms "mounted," "connected," and "fixed" as used in the examples should be interpreted broadly, e.g., the connection may be a fixed connection, a removable connection, or an integral, it should be understood that the connection may be a mechanical connection, an electrical connection, or the like, or of course, the connection may be direct, or indirect, through an intermediary, or may be a communication between two elements, or an interaction relationship between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to specific embodiments.
In the present invention, unless expressly stated or limited otherwise, a first feature "up" or "down" a second feature may be the first and second features in direct contact, or the first and second features in indirect contact via an intervening medium. Moreover, a first feature being "above," "over" and "on" a second feature may be a first feature being directly above or obliquely above the second feature, or simply indicating that the first feature is level higher than the second feature. The first feature being "under", "below" and "beneath" the second feature may be the first feature being directly under or obliquely below the second feature, or simply indicating that the first feature is less level than the second feature.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (8)
1. An application method of an anesthesia cabinet based on fingerprint identification is characterized by comprising the following steps:
S100, acquiring a fingerprint picture through an optical fingerprint sensor;
S200, graying the fingerprint picture to obtain a fingerprint gray-scale picture;
s300, acquiring a distance characteristic line according to the fingerprint gray level diagram;
S400, obtaining a ridge-valley characteristic distance measurement value and a ridge-valley characteristic difference value through a distance characteristic line, and storing the ridge-valley characteristic distance measurement value and the ridge-valley characteristic difference value;
S500, when the anesthesia cabinet needs to be opened, acquiring a ridge-valley characteristic distance measurement value and ridge-valley characteristic difference quantity of a user to be identified, and judging whether to open the anesthesia cabinet according to the stored ridge-valley characteristic distance measurement value and ridge-valley characteristic difference quantity.
2. The method of claim 1, wherein in step S100, the optical fingerprint sensor is used to acquire a fingerprint image, the optical fingerprint sensor comprises an optical scanning module and an image sensor, the optical sensor is used to capture the fingerprint image by projecting light onto the fingerprint, the user places the finger on the sensor, and the sensor is used to optically acquire the fingerprint image to generate the original fingerprint image.
3. The method for applying a fingerprint-based anesthesia cabinet according to claim 1, wherein in step S200, the process of graying the fingerprint image to obtain a fingerprint gray image includes:
The fingerprint image is subjected to graying processing, the fingerprint image after the graying processing is recorded as a first gray image, edges in the image are detected through a Canny edge detection algorithm, connected areas in the gray image are identified and filled to determine main characteristic areas of fingerprints, wherein the process of identifying and filling the connected areas in the gray image comprises the steps of scanning the image line by line, finding out each connected area, distributing a unique label for each connected area, wherein the connected areas are areas with the same pixel value in the gray image, and highlighting the areas by filling all pixels in the connected areas to be specific values, wherein the area filling method comprises the steps of starting from an initial point, recursively filling adjacent pixels to the area boundary, and obtaining characteristic area sets containing fingerprint ridges and valley lines after the edge detection and the area filling processing, wherein the characteristic areas of the fingerprint ridges and the valley lines represent key details in the fingerprint image, so that subsequent fingerprint analysis and matching are facilitated.
4. The fingerprint identification based anesthesia cabinet application method according to claim 1, wherein in step S300, acquiring a distance feature line according to a fingerprint gray-scale image comprises:
S301, using zj (j) to represent the gray value of the grid of the characteristic region of the jth fingerprint ridge, wherein the value range of j is [1, K ], wherein K is the number of the grids of the characteristic region of the fingerprint ridge, zg (g) represents the gray value of the grid of the characteristic region of the jth fingerprint valley, g is [1, H ], wherein H is the number of the grids of the characteristic region of the fingerprint valley, the gray value of the grid is the average value of the gray values of all pixels in the grid, the grid of the characteristic region of the fingerprint ridge with the maximum gray value of the grid is taken as zjxu, the gray value of zjxu is taken as zjxp, and the grid of the characteristic region of the fingerprint valley with the maximum gray value of the grid is taken as zgxu, and the gray value of zgxu is taken as zgxp;
S302, defining an integer variable k, setting an initial value to 1, and creating two variables HDzh and HDzh2 which are initially zero for subsequent calculation and comparison;
S303, gray level characteristic analysis of gray level values is carried out on grids of the characteristic areas of the fingerprint ridge lines and grids of the characteristic areas of the fingerprint valley lines, and two blank sequences HDa and HDb are created;
S304, calculating values of HDzh1 and HDzh2, wherein HDzh1 has a calculation formula of zjxp-zj (k), HDzh has a calculation formula of zgxp-zg (k), comparing values of HDzh and HDzh, wherein if HDzh1 is larger than HDzh2, a grid sequence number where zj (j) is located is added to a sequence HDa, and if HDzh1 is smaller than or equal to HDzh2, a grid sequence number where zg (g) is located is added to a sequence HDb;
S305, judging whether the gray feature analysis is finished, if the current variable K is smaller than K or smaller than H, increasing K by 1, returning to the step S303, and continuing the gray feature analysis, and if the current variable j is not smaller than K or not smaller than H, indicating that the gray feature analysis is finished;
S306, acquiring the gray value under the grid corresponding to the grid serial number in the sequence HDa as the median of the gray values under the grids corresponding to all the grid serial numbers in the sequence HDa, marking the grid as zjx, acquiring the gray value under the grid corresponding to the grid serial number in the sequence HDb as the median of the gray values under the grids corresponding to all the grid serial numbers in the sequence HDb, and marking the grid as zgx;
S307, obtaining the distance between grids of the characteristic areas of all fingerprint ridges, wherein the distance between grids is the length of a line segment, which is connected with a grid center point, of the grid of the characteristic area of one fingerprint ridge and the grid of the characteristic area of all other fingerprint ridges, and the shortest distance between the grid of the characteristic area of the j fingerprint ridge and the grid of the other fingerprint ridge is recorded as the shortest adjacent distance;
S308, obtaining distance characteristic lines, wherein the distance characteristic lines comprise a first distance characteristic line, a second distance characteristic line and a third distance characteristic line, specifically, a midpoint P3 of a line segment between zgx and zjx is recorded, a center point of a distance grid zjt is recorded as P4, a grid center point of a distance grid zjp is recorded as P5, a midpoint of a line segment between the points P4 and P5 is recorded as P6, a line segment between the points P3 and P6 is a first distance characteristic line L1, a line segment between the points P3 and P4 is a second distance characteristic line L2, and a line segment between the points P3 and P5 is a third distance characteristic line L3.
5. The fingerprint recognition-based anesthesia cabinet application method according to claim 4, wherein in step S400, obtaining the ridge-valley characteristic distance metric and the ridge-valley characteristic difference amount through the distance characteristic line, and storing the ridge-valley characteristic distance metric and the ridge-valley characteristic difference amount comprises:
The method comprises the steps of obtaining a length of L1, namely a ridge-valley characteristic distance measurement value lg, obtaining a length of L2, namely a first characteristic length lka, obtaining a length of L3, namely a second characteristic length lkb, and calculating ridge-valley characteristic differential value ld, wherein ld=abs (lka-lkb), wherein abs is an absolute value function, namely the ridge-valley characteristic differential value ld is an absolute value of a direct difference value between the first characteristic length lka and the second characteristic length lkb.
6. The fingerprint identification-based anesthesia cabinet application method according to claim 1, wherein in step S500, when the anesthesia cabinet needs to be opened, the method for acquiring the distance measurement value of the ridge-valley characteristic and the difference amount of the ridge-valley characteristic of the user to be identified, and judging whether to open the anesthesia cabinet according to the stored distance measurement value of the ridge-valley characteristic and the difference amount of the ridge-valley characteristic comprises:
The user to be identified is a user who needs to open the anesthesia cabinet;
And if the ridge-valley characteristic distance measurement value of the user to be identified is not satisfied with the ridge-valley characteristic distance measurement value of the user to be identified and the stored ridge-valley characteristic distance measurement value lg are equal, and the ridge-valley characteristic difference value ld of the user to be identified are both equal, the anesthesia cabinet is not opened, and the identification failure is displayed.
7. The fingerprint identification-based anesthesia cabinet application method according to claim 1, wherein in the step S500, when the anesthesia cabinet needs to be opened, the method for judging whether to open the anesthesia cabinet or not according to the stored ridge-valley characteristic distance metric and the stored ridge-valley characteristic differential is further characterized in that when the ridge-valley characteristic distance metric of the user to be identified is equal to the stored ridge-valley characteristic distance metric lg and the ridge-valley characteristic differential ld of the user to be identified is equal to each other, the anesthesia cabinet is opened and the identification is successful, if the ridge-valley characteristic distance metric of the user to be identified is not equal to the stored ridge-valley characteristic distance metric lg and the ridge-valley characteristic differential ld of the user to be identified is equal to each other, the absolute value of the difference between the ridge-valley characteristic distance metric and the ridge-valley characteristic distance metric lg of the user to be identified is calculated and is a dislocation interval difference, if the dislocation interval difference is smaller than the stored ridge-valley characteristic distance metric ld of the user to be identified, and if the dislocation difference is not smaller than the dislocation threshold is not successfully opened, the anesthesia cabinet is not identified, and the dislocation threshold is not successfully opened.
8. An anesthesia cabinet characterized in that the anesthesia cabinet is identified and opened by the fingerprint identification based anesthesia cabinet application method as claimed in any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411239606.0A CN119206799B (en) | 2024-09-05 | 2024-09-05 | Anesthesia cabinet application method based on fingerprint recognition and anesthesia cabinet |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411239606.0A CN119206799B (en) | 2024-09-05 | 2024-09-05 | Anesthesia cabinet application method based on fingerprint recognition and anesthesia cabinet |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN119206799A true CN119206799A (en) | 2024-12-27 |
| CN119206799B CN119206799B (en) | 2025-04-11 |
Family
ID=94075825
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202411239606.0A Active CN119206799B (en) | 2024-09-05 | 2024-09-05 | Anesthesia cabinet application method based on fingerprint recognition and anesthesia cabinet |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN119206799B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002298126A (en) * | 2001-03-29 | 2002-10-11 | Fujitsu Ltd | Input fingerprint state determination method and device |
| US20060228006A1 (en) * | 2000-01-28 | 2006-10-12 | Chuo Hatsujo Kabushiki Kaisha | Fingerprint image evaluation method and fingerprint verification device |
| KR20170061424A (en) * | 2015-11-26 | 2017-06-05 | 한국과학기술원 | Method for Certificating Real Living Body Micro Sweating |
| CN116188024A (en) * | 2023-04-24 | 2023-05-30 | 山东蓝客信息科技有限公司 | Medical safety payment system |
| CN117218691A (en) * | 2023-11-09 | 2023-12-12 | 四川酷比通信设备有限公司 | Unlocking method based on fingerprint identification |
-
2024
- 2024-09-05 CN CN202411239606.0A patent/CN119206799B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060228006A1 (en) * | 2000-01-28 | 2006-10-12 | Chuo Hatsujo Kabushiki Kaisha | Fingerprint image evaluation method and fingerprint verification device |
| JP2002298126A (en) * | 2001-03-29 | 2002-10-11 | Fujitsu Ltd | Input fingerprint state determination method and device |
| KR20170061424A (en) * | 2015-11-26 | 2017-06-05 | 한국과학기술원 | Method for Certificating Real Living Body Micro Sweating |
| CN116188024A (en) * | 2023-04-24 | 2023-05-30 | 山东蓝客信息科技有限公司 | Medical safety payment system |
| CN117218691A (en) * | 2023-11-09 | 2023-12-12 | 四川酷比通信设备有限公司 | Unlocking method based on fingerprint identification |
Non-Patent Citations (1)
| Title |
|---|
| 孙林森;吴小培;项明;: "一种有效的指纹图像预处理方法", 计算机技术与发展, no. 02, 10 February 2009 (2009-02-10) * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN119206799B (en) | 2025-04-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Liu et al. | 3D fingerprint reconstruction system using feature correspondences and prior estimated finger model | |
| US5610993A (en) | Method of co-centering two images using histograms of density change | |
| US7983451B2 (en) | Recognition method using hand biometrics with anti-counterfeiting | |
| JP2776409B2 (en) | Classification device for skin pattern and fingerprint pattern | |
| US8391590B2 (en) | System and method for three-dimensional biometric data feature detection and recognition | |
| US8811681B2 (en) | Biometric authentication apparatus and biometric authentication method | |
| US8442276B2 (en) | Invariant radial iris segmentation | |
| US20100232659A1 (en) | Method for fingerprint template synthesis and fingerprint mosaicing using a point matching algorithm | |
| CN107066961B (en) | Fingerprint registration method and device | |
| US7885437B2 (en) | Fingerprint collation apparatus, fingerprint pattern area extracting apparatus and quality judging apparatus, and method and program of the same | |
| JP2002514824A (en) | Creating reference images for pattern recognition tasks | |
| US20080101662A1 (en) | Print matching method and apparatus using pseudo-ridges | |
| JP2020080188A (en) | Fingerprint information processor, fingerprint information processing method, and program | |
| US10853620B2 (en) | Dermal image information processing device, dermal image information processing method, and program | |
| US20130004031A1 (en) | Biometric information processing apparatus, method, and recording medium | |
| US20070292005A1 (en) | Method and apparatus for adaptive hierarchical processing of print images | |
| Bolle et al. | Fingerprint minutiae: A constructive definition | |
| JP4003465B2 (en) | Specific pattern recognition method, specific pattern recognition program, specific pattern recognition program recording medium, and specific pattern recognition apparatus | |
| WO2008143271A1 (en) | Registering device, checking device, program, and data structure | |
| CN119206799B (en) | Anesthesia cabinet application method based on fingerprint recognition and anesthesia cabinet | |
| US20060002599A1 (en) | Generation of directional field information in the context of image processing | |
| Shin et al. | Detecting fingerprint minutiae by run length encoding scheme | |
| KR20070088982A (en) | Robust Recognition Method for Robust Deformation | |
| CN113673477A (en) | Palm vein non-contact three-dimensional modeling method and device and authentication method | |
| EP4328874A1 (en) | Method for verifying the authenticity of a fingerprint |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |