CN110414314A - A kind of camera structure carrying out Face tracking and recognition and system - Google Patents
A kind of camera structure carrying out Face tracking and recognition and system Download PDFInfo
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- CN110414314A CN110414314A CN201910501445.0A CN201910501445A CN110414314A CN 110414314 A CN110414314 A CN 110414314A CN 201910501445 A CN201910501445 A CN 201910501445A CN 110414314 A CN110414314 A CN 110414314A
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- 238000012795 verification Methods 0.000 claims description 9
- 238000001514 detection method Methods 0.000 claims description 7
- 238000000605 extraction Methods 0.000 claims description 6
- 210000000056 organ Anatomy 0.000 claims description 5
- 230000005540 biological transmission Effects 0.000 claims description 4
- 229910000838 Al alloy Inorganic materials 0.000 claims description 3
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- 210000000887 face Anatomy 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 238000003909 pattern recognition Methods 0.000 description 2
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- G—PHYSICS
- G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
- G03B—APPARATUS OR ARRANGEMENTS FOR TAKING PHOTOGRAPHS OR FOR PROJECTING OR VIEWING THEM; APPARATUS OR ARRANGEMENTS EMPLOYING ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ACCESSORIES THEREFOR
- G03B17/00—Details of cameras or camera bodies; Accessories therefor
- G03B17/56—Accessories
- G03B17/561—Support related camera accessories
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/10—Image acquisition
- G06V10/12—Details of acquisition arrangements; Constructional details thereof
- G06V10/14—Optical characteristics of the device performing the acquisition or on the illumination arrangements
- G06V10/147—Details of sensors, e.g. sensor lenses
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- 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
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/161—Detection; Localisation; Normalisation
- G06V40/166—Detection; Localisation; Normalisation using acquisition arrangements
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Abstract
The invention discloses camera structures and system that one kind can carry out Face tracking and recognition, including the first Servo-controller and the second Servo-controller, the first Servo-controller lower rotation output end is fixedly connected with camera bracket, second Servo-controller is fixedly mounted on camera bracket side, the rotation output end of second Servo-controller is fixedly installed with camera, it include camera system in the camera, the camera system includes face recognition module, face tracking module and prompt system, the face recognition module, face tracking module and the equal signal of prompt system are connected to system in camera, the camera system is electrically connected with servo motor, the present invention can judge identification subject face position by face tracking module, the movement of camera both horizontally and vertically is driven using the movement of Servo-controller , realize to face tracking, face recognition module recycled to identify face, improve recognition of face success rate.
Description
Technical field
The present invention relates to technical field of face recognition, specially a kind of camera structure that can carry out Face tracking and recognition.
Background technique
Recognition of face is a kind of biological identification technology for carrying out identification based on facial feature information of people.With camera shooting
Machine or camera acquire image or video flowing containing face, and detect face in the picture automatically, and then to the people detected
Face carries out a series of the relevant technologies of face recognition, usually also referred to as Identification of Images, face recognition.
Currently, face recognition technology with more and more extensive, but due to different identification object face locations exist it is poor
It is different, it may appear that recognition of face failure, the low problem of recognition success rate.Based on this background, the present invention proposes that one kind can carry out face
The camera structure and system tracked and identified can carry out Face tracking and recognition camera structure, improve the success rate of recognition of face.
Summary of the invention
The purpose of the present invention is to provide camera structures and system that one kind can carry out Face tracking and recognition, pass through face
Tracking module judges identification subject face position, drives camera both horizontally and vertically using the movement of Servo-controller
Movement, realize to face tracking, recycle face recognition module to identify face, it can be achieved that Face tracking and recognition images
Header structure；The judgement recognition principle of Face tracking and recognition improves recognition of face success rate, to solve to propose in above-mentioned background technique
The problem of.
To achieve the above object, the invention provides the following technical scheme: a kind of camera that can carry out Face tracking and recognition
Structure, including the first Servo-controller and the second Servo-controller, the first Servo-controller lower rotation output end are fixedly connected with
Camera bracket, second Servo-controller are fixedly mounted on camera bracket side, and the rotation of second Servo-controller is defeated
Outlet is fixedly installed with camera.
Preferably, first Servo-controller and the second Servo-controller are made of motor, transmission parts and clutch.
Preferably, the camera bracket is aluminium alloy post structure.
The present invention also provides the camera system that one kind can carry out Face tracking and recognition, including camera, the cameras
Interior includes camera system, and the camera system includes face recognition module, face tracking module and prompt system, the people
Face identification module, face tracking module and the equal signal of prompt system are connected to system in camera, and the camera system is electrical
It is connected with servo motor.
Preferably, the face recognition module includes Face datection, face tracking and face alignment, face by technical principle
The techniqueflow of identification module includes four component parts, be respectively as follows: man face image acquiring and detection, facial image pretreatment,
Facial image feature extraction and matching and identification.
Preferably, the face recognition module includes two kinds of comparison modes of verification formula and search type, and verification formula is will to finger
Capture that registered in obtained portrait or specified portrait and database certain is a pair of as the verification that compares determines whether it is
The comparison of same people, search type refer to that search searches whether specified portrait from all portraits registered in database
In the presence of.
Preferably, the face tracking module includes face capture and face tracking, face capture be in piece image or
Face is detected in one frame of video flowing and separates face from background, and is automatically saved, and face tracking is
Using portrait capture technique, automatically it is tracked when specified face moves in the range of camera is shot.
Preferably, the prompt system includes acousto-optic hint module and voice broadcast module.
Preferably, the servo motor is dc motor, converts torque and revolving speed for voltage signal with drive control
Object refers to the engine for controlling mechanical organ operating in servo-system, is a kind of indirect speed change gear of subsidy motor.
Compared with prior art, the beneficial effects of the present invention are:
1, the present invention can judge identification subject face position by face tracking module, utilize the movement of Servo-controller
The movement of camera both horizontally and vertically is driven, realizes to face tracking, face recognition module is recycled to know face
Not, it can be achieved that Face tracking and recognition camera structure；The judgement recognition principle of Face tracking and recognition improves recognition of face success
Rate.
Detailed description of the invention
Fig. 1 is the camera system block diagram that one kind of the present invention can carry out Face tracking and recognition；
Fig. 2 is the camera structure main view that one kind of the present invention can carry out Face tracking and recognition；
Fig. 3 is the camera structure right view that one kind of the present invention can carry out Face tracking and recognition；
Fig. 4 is the camera structure dorsal view that one kind of the present invention can carry out Face tracking and recognition；
Fig. 5 is the Face tracking and recognition flow chart for the camera system that one kind of the present invention can carry out Face tracking and recognition.
In figure: 1, the first Servo-controller；2, camera bracket；3, camera；4, the second Servo-controller.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Fig. 1-5 is please referred to, the present invention provides a kind of technical solution: one kind can carry out the camera knot of Face tracking and recognition
Structure, including the first Servo-controller 1 and the second Servo-controller 4, the 1 lower rotation output end of the first Servo-controller are fixedly connected with
Camera bracket 2, second Servo-controller 4 are fixedly mounted on 2 side of camera bracket, and second Servo-controller 4 turns
Dynamic output end is fixedly installed with camera 3.
Specifically, first Servo-controller 1 and the second Servo-controller 4 are by motor, transmission parts and clutch group
At；Transmission parts are the component that output shaft is connected by shaft coupling, for connecting with other structures.
Specifically, the camera bracket 2 is aluminium alloy post structure.
The present invention also provides the camera system that one kind can carry out Face tracking and recognition, including camera, the cameras
Interior includes camera system, and the camera system includes face recognition module, face tracking module and prompt system, the people
Face identification module, face tracking module and the equal signal of prompt system are connected to system in camera, and the camera system is electrical
It is connected with servo motor.
Specifically, the face recognition module includes Face datection, face tracking and face alignment, face by technical principle
The techniqueflow of identification module includes four component parts, be respectively as follows: man face image acquiring and detection, facial image pretreatment,
Facial image feature extraction and matching and identification.
(1) Face datection
Looks detection, which refers to, judges whether there is image surface in dynamic scene and complicated background, and isolates this image surface.
Generally there is following several method:
1. reference template method
The template of one or several standard faces is designed first, then calculates between the sample and standard form of test acquisition
With degree, and pass through threshold value to determine whether there are faces；
2. face rule method
Since face has certain structure distribution feature, the method for so-called face rule is extracted these features and is generated accordingly
Rule is to judge whether test sample includes face；
3. sample learning method
This method is the method for using artificial neural network in pattern-recognition, i.e., by opposite as sample sets and the decent product of non-face
The study of collection generates classifier；
4. complexion model method
This method is to be distributed the rule of Relatively centralized in color space according to the looks colour of skin to be detected.
5. sub-face of feature method
This method be all image surface set are considered as to an image surface subspace, and based on test sample and its subspace throwing
The distance between shadow judges whether there is image surface.
It is worth mentioning that above-mentioned 5 kinds of methods can also integrate use in actually detected system.
(2) face tracking
Looks tracking, which refers to, carries out dynamic target tracking to the looks being detected.The specific method used based on model is based on
Move the method combined with model.In addition, tracking a kind of simple and effective means of also can yet be regarded as using complexion model.
(3) face alignment
It is to carry out identity validation to the looks picture that is detected or carry out target search in image surface library that looks, which compare,.This is actually
That is the image surface sampled is successively compared with the image surface of inventory, and find out optimal matching object.So image surface
Description determine the specific method and performance of face recognizing.Mainly using feature vector and two kinds of description methods of face line template:
1. feature vector method
This method is first attributes such as size, position, distance of the image surfaces such as determining eye iris, the wing of nose, corners of the mouth face profile, then again
Their geometric feature is calculated, and these characteristic quantities form a feature vector for describing the image surface.
2. face line template
This method is to store several standard image surface templates or image surface organ template in library to sample image surface when being compared
All pixels are matched with templates all in library using normalization correlative measurement.In addition, also using oneself of pattern-recognition
The method that network of relation or feature are combined with template.
The practical core of face recognition technology is " partial body's signature analysis " and " figure/nerve recognizer." this
Algorithm is the method using each organ of human body face and characteristic portion.Geometrical relationship majority is such as corresponded to according to formation identification parameter and number
It is compared, judges and confirmation according to initial parameter all in library.It is general to require to judge the time lower than 1 second.
Man face image acquiring and detection
Man face image acquiring: different facial images can be transferred through pick-up lens and collect, such as still image, Dynamic Graph
Picture, different positions, different expressions etc. can be acquired well.When user is in the coverage of acquisition equipment
When, acquisition equipment can search for automatically and shoot the facial image of user.
Face datection: Face datection is mainly used for the pretreatment of recognition of face in practice, i.e. accurate calibration in the picture
The position of face and size out.The pattern feature very abundant for including in facial image, such as histogram feature, color characteristic, mould
Plate features, structure feature and Haar feature etc..Face datection is exactly information useful among these to be picked out, and utilize these spies
Levies in kind shows Face datection.
The method for detecting human face of mainstream is based on features above and uses Adaboost learning algorithm, and Adaboost algorithm is a kind of
For the method classified, it is combined some weaker classification methods, is combined into new very strong classification method.
Some rectangular characteristic (weak typings that can most represent face are picked out using Adaboost algorithm during Face datection
Device), Weak Classifier is configured to a strong classifier, then several strong classifiers that training is obtained in the way of Nearest Neighbor with Weighted Voting
It is composed in series the cascade filtering of a cascade structure, effectively improves the detection speed of classifier.
Facial image pretreatment: the image preprocessing for face is based on Face datection as a result, handling image
And finally serve the process of feature extraction.The original image that system obtains by various conditions due to being limited and being done at random
It disturbs, tends not to directly use, it is necessary to which it is pre- to carry out the images such as gray correction, noise filtering to it in the early stage of image procossing
Processing.For facial image, preprocessing process mainly includes light compensation, the greyscale transformation, histogram of facial image
Equalization, normalization, geometric correction, filtering and sharpening etc..
Facial image feature extraction: it is special that feature workable for face identification system is generally divided into visual signature, pixels statistics
Sign, facial image transformation coefficient feature, facial image algebraic characteristic etc..Face characteristic extracts certain spies aiming at face
What sign carried out.Face characteristic extracts, and also referred to as face characterizes, it is the process that feature modeling is carried out to face.Face characteristic extracts
Method be summed up and be divided into two major classes: one is Knowledge based engineering characterizing methods；Another is based on algebraic characteristic or system
Count the characterizing method of study.
Knowledge based engineering characterizing method mainly according to the shape description of human face and they the distance between characteristic
The characteristic for facilitating face classification is obtained, characteristic component generally includes Euclidean distance, curvature and angle between characteristic point
Degree etc..Face is locally made of eyes, nose, mouth, chin etc., and to these parts and the geometry of structural relation is retouched between them
It states, can be used as the important feature of identification face, these features are referred to as geometrical characteristic.The main packet of Knowledge based engineering face characterization
Include method and template matching method based on geometrical characteristic.
Facial image matching and identification: the feature templates stored in the characteristic and database of the facial image of extraction into
Row search matching, by setting a threshold value, when similarity is more than this threshold value, then result matching obtained is exported.Face
Identification is exactly to be compared face characteristic to be identified with obtained skin detection, according to similarity degree to face
Identity information is judged.This process is divided into two classes again: one kind is confirmation, is the one-to-one process for carrying out image comparison, separately
One kind is identification, is the one-to-many process for carrying out images match comparison.
Specifically, the face recognition module includes two kinds of comparison modes of verification formula and search type, verification formula is will to finger
Capture that registered in obtained portrait or specified portrait and database certain is a pair of as the verification that compares determines whether it is
The comparison of same people, search type refer to that search searches whether specified portrait from all portraits registered in database
In the presence of.
Specifically, the face tracking module includes face capture and face tracking, face capture be in piece image or
Face is detected in one frame of video flowing and separates face from background, and is automatically saved, and face tracking is
Using portrait capture technique, automatically it is tracked when specified face moves in the range of camera is shot.
Specifically, the prompt system includes acousto-optic hint module and voice broadcast module.
Specifically, the servo motor is dc motor, torque and revolving speed are converted with drive control by voltage signal
Object refers to the engine for controlling mechanical organ operating in servo-system, is a kind of indirect speed change gear of subsidy motor.
Working principle: in use, opening camera 3；Face tracking module judges whether face is in camera image
Center, if the not heart in the picture, by the first Servo-controller 1 and the second Servo-controller 4 drives respectively the level of camera with
The movement of vertical direction, once face reaches center for stop motion, face recognition module identifies face, prompt system
Prompt user's recognition of face situation.
It although an embodiment of the present invention has been shown and described, for the ordinary skill in the art, can be with
A variety of variations, modification, replacement can be carried out to these embodiments without departing from the principles and spirit of the present invention by understanding
And modification, the scope of the present invention is defined by the appended.
Claims (9)
1. one kind can carry out the camera structure of Face tracking and recognition, including the first Servo-controller (1) and the second Servo-controller
(4), it is characterised in that: the first Servo-controller (1) lower rotation output end is fixedly connected with camera bracket (2), described
Second Servo-controller (4) is fixedly mounted on camera bracket (2) side, and the rotation output end of second Servo-controller (4) is solid
Dingan County is equipped with camera (3).
2. the camera structure that one kind according to claim 1 can carry out Face tracking and recognition, it is characterised in that: described
One Servo-controller (1) and the second Servo-controller (4) are made of motor, transmission parts and clutch.
3. the camera structure that one kind according to claim 1 can carry out Face tracking and recognition, it is characterised in that: described to take the photograph
As head bracket (2) are aluminium alloy post structure.
4. a kind of camera system according to claim 1 for carrying out Face tracking and recognition, including camera, feature
Be: including camera system in the camera, the camera system include face recognition module, face tracking module and
Prompt system, the face recognition module, face tracking module and the equal signal of prompt system are connected to system in camera, described
Camera system is electrically connected with servo motor.
5. the camera system that one kind according to claim 4 can carry out Face tracking and recognition, it is characterised in that: the people
Face identification module includes Face datection, face tracking and face alignment, the techniqueflow packet of face recognition module by technical principle
Include four component parts, be respectively as follows: man face image acquiring and detection, facial image pretreatment, facial image feature extraction and
Matching and identification.
6. the camera system that one kind according to claim 4 can carry out Face tracking and recognition, it is characterised in that: the people
Face identification module includes two kinds of comparison modes of verification formula and search type, and verification formula is the portrait for obtaining capture to finger or specifies
Portrait and database in it is registered certain is a pair of as the verification that compares determines whether it is same people, the comparison of search type is
Refer to, searches for and searched whether with the presence of specified portrait from all portraits registered in database.
7. the camera system that one kind according to claim 4 can carry out Face tracking and recognition, it is characterised in that: the people
Face tracking module includes face capture and face tracking, and face capture is to detect people in a frame of piece image or video flowing
Face simultaneously separates face from background, and is automatically saved, and face tracking is using portrait capture technique, when specified
Face camera shoot in the range of move when automatically it is tracked.
8. the camera system that one kind according to claim 4 can carry out Face tracking and recognition, it is characterised in that: described to mention
Show that system includes acousto-optic hint module and voice broadcast module.
9. the camera system that one kind according to claim 4 can carry out Face tracking and recognition, it is characterised in that: described to watch
Taking motor is dc motor, converts torque and revolving speed for voltage signal with drive control object, refers in servo-system
The engine for controlling mechanical organ operating, is a kind of indirect speed change gear of subsidy motor.
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| CN111582112A (en) * | 2020-04-29 | 2020-08-25 | 重庆工程职业技术学院 | Working equipment and working method for screening abnormal personnel aiming at dense people |
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| CN111783588A (en) * | 2020-06-23 | 2020-10-16 | 大连中维世纪科技有限公司 | Distributed intelligent passenger flow statistics effective de-duplication method |
| CN111860428A (en) * | 2020-07-30 | 2020-10-30 | 上海华虹计通智能系统股份有限公司 | Face recognition system and method |
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| CN111898554B (en) * | 2020-07-31 | 2023-06-23 | 重庆广播电视大学重庆工商职业学院 | Working system and working method for video image capture based on feature points |
| CN111898554A (en) * | 2020-07-31 | 2020-11-06 | 重庆广播电视大学重庆工商职业学院 | Working system and working method for video image capture based on feature points |
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| CN113076915B (en) * | 2021-04-19 | 2024-02-02 | 北京交通大学 | Face recognition device for machine learning |
| CN113076915A (en) * | 2021-04-19 | 2021-07-06 | 北京交通大学 | Face recognition device for machine learning |
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