Biometric systems based on iris are vulnerable to several attacks, particularly direct attacks co... more Biometric systems based on iris are vulnerable to several attacks, particularly direct attacks consisting on the presen-tation of a fake iris to the sensor. The development of iris liveness detection techniques is crucial for the deployment of iris biometric applications in daily life specially in the mobile biometric field. The 1st Mobile Iris Liveness Detec-tion Competition (MobILive) was organized in the context of IJCB2014 in order to record recent advances in iris live-ness detection. The goal for (MobILive) was to contribute to the state of the art of this particular subject. This com-petition covered the most common and simple spoofing at-tack in which printed images from an authorized user are presented to the sensor by a non-authorized user in order to obtain access. The benchmark dataset was the MobBIOfake database which is composed by a set of 800 iris images and its corresponding fake copies (obtained from printed images of the original ones captured with the same handhe...
Abstract—The new challenges concerning iris recognition are based on the need to support less con... more Abstract—The new challenges concerning iris recognition are based on the need to support less constrained image acquisition environments. The proposed algorithm aims to achieve robust iris segmentation, based on combined information from iris center and iris contour, with such images. A 5.72 % mean error was obtained for the outer contour segmentation while the pupillary region presented a 9.36 % mean pixel misclassification. I.
The development of presentation attack detection (PAD) methods has become a high level concern in... more The development of presentation attack detection (PAD) methods has become a high level concern in biometric security. As in other pattern recognition tasks, the use of deep learning is increasingly common. However, it is still doubtful if handcrafted features should be discarded. This work focused on the comparison of using handcrafted features and deep learning techniques at the feature extraction level in a face PAD method. Handcrafted features were based on Local Binary Patterns, while a Convolutional Neural Network based on VGG-16 was used for deep feature extraction. A Support Vector Machine was used for binary classification after dimensionality reduction using Principal Component Analysis. The methods were tested using the NUAA database, and the results show that handcrafted feature extraction still offer better results, with 3.1% APCER and 25.2% BPCER.
This work presents a novel multimodal database comprising 3D face, 2D face, thermal face, visible... more This work presents a novel multimodal database comprising 3D face, 2D face, thermal face, visible iris, finger and hand veins, voice and anthropometrics. This dataset will constitute a valuable resource to the field with its number and variety of biometric traits. Acquired in the context of the EU PROTECT project, the dataset allows several combinations of biometric traits and envisages applications such as border control. Based upon the results of the unimodal data, a fusion scheme was applied to ascertain the recognition potential of combining these biometric traits in a multimodal approach. Due to the variability on the discriminative power of the traits, a leave the n-best out fusion technique was applied to obtain different recognition results.
Biometrics represents a return to a traditional way of identifying someone relying on what that p... more Biometrics represents a return to a traditional way of identifying someone relying on what that person is instead of what that person knows or owns. Even though the significant amount of research that has been done in this field, there is still much to do as new emerging scenarios of application appear everyday. Biometric recognition systems are no longer restricted to forensic investigation or control management of employees. They have been gaining a visibility and applicability in daily use devices which reinforces their usability in all aspects of our day to day life. With this spread of biometric applications, nowadays commonly found in our laptops, our smart phones, some bank management services and airport custom services, a necessity for improved security also is rising. The importance of protecting our identity and our data has become crucial as our devices are filled with sensible information of many kinds. Therefore the presentation attack or liveness detection methods as ...
2020 International Conference of the Biometrics Special Interest Group (BIOSIG), 2020
Fingerprint presentation attack detection (PAD) methods present a stunning performance in current... more Fingerprint presentation attack detection (PAD) methods present a stunning performance in current literature. However, the fingerprint PAD generalisation problem is still an open challenge requiring the development of methods able to cope with sophisticated and unseen attacks as our eventual intruders become more capable. This work addresses this problem by applying a regularisation technique based on an adversarial training and representation learning specifically designed to to improve the PAD generalisation capacity of the model to an unseen attack. In the adopted approach, the model jointly learns the representation and the classifier from the data, while explicitly imposing invariance in the high-level representations regarding the type of attacks for a robust PAD. The application of the adversarial training methodology is evaluated in two different scenarios: i) a handcrafted feature extraction method combined with a Multilayer Perceptron (MLP); and ii) an end-to-end solution ...
Despite the high performance of current presentation attack detection (PAD) methods, the robustne... more Despite the high performance of current presentation attack detection (PAD) methods, the robustness to unseen attacks is still an under addressed challenge. This work approaches the problem by enforcing the learning of the bona fide presentations while making the model less dependent on the presentation attack instrument species (PAIS). The proposed model comprises an encoder, mapping from input features to latent representations, and two classifiers operating on these underlying representations: (i) the task-classifier, for predicting the class labels (as bona fide or attack); and (ii) the species-classifier, for predicting the PAIS. In the learning stage, the encoder is trained to help the task-classifier while trying to fool the species-classifier. Plus, an additional training objective enforcing the similarity of the latent distributions of different species is added leading to a ‘PAI-species’-independent model. The experimental results demonstrated that the proposed regularisat...
The spread of biometric applications in mobile devices handled by untrained users opened the door... more The spread of biometric applications in mobile devices handled by untrained users opened the door to sources of noise in mobile iris recognition such as larger extent of rotation in the capture and more off-angle imagery not found so extensively in more constrained acquisition settings. As a result of the limitations of the methods in handling such large degrees of freedom there is often an increase in segmentation errors. In this work, a new near-infrared iris dataset captured with a mobile device is evaluated to analyse, in particular, the rotation observed in images and its impact on segmentation and biometric recognition accuracy. For this study a (manually annotated) ground truth segmentation was used which will be published in tandem with the paper. Similarly to most research challenges in biometrics and computer vision in general, deep learning techniques are proving to outperform classical methods in segmentation methods. The utilization of parameterized CNN-based iris segme...
In recent years many authors have recognized that the path forward, regarding iris recognition, i... more In recent years many authors have recognized that the path forward, regarding iris recognition, is the development of iris recognition systems that can work independently of the conditions under which iris images are acquired. Recent works have tried to achieve robust and unconstrained iris recognition in order to develop real-world applicable methods. In this work, the problem of unconstrained iris recognition is referred and some results of an approach to the problem of fusing color information to enhance the performance of an iris authentication system are briefly presented.
Biometric systems based on iris are vulnerable to several attacks, particularly direct attacks co... more Biometric systems based on iris are vulnerable to several attacks, particularly direct attacks consisting on the presen-tation of a fake iris to the sensor. The development of iris liveness detection techniques is crucial for the deployment of iris biometric applications in daily life specially in the mobile biometric field. The 1st Mobile Iris Liveness Detec-tion Competition (MobILive) was organized in the context of IJCB2014 in order to record recent advances in iris live-ness detection. The goal for (MobILive) was to contribute to the state of the art of this particular subject. This com-petition covered the most common and simple spoofing at-tack in which printed images from an authorized user are presented to the sensor by a non-authorized user in order to obtain access. The benchmark dataset was the MobBIOfake database which is composed by a set of 800 iris images and its corresponding fake copies (obtained from printed images of the original ones captured with the same handhe...
Abstract—The new challenges concerning iris recognition are based on the need to support less con... more Abstract—The new challenges concerning iris recognition are based on the need to support less constrained image acquisition environments. The proposed algorithm aims to achieve robust iris segmentation, based on combined information from iris center and iris contour, with such images. A 5.72 % mean error was obtained for the outer contour segmentation while the pupillary region presented a 9.36 % mean pixel misclassification. I.
The development of presentation attack detection (PAD) methods has become a high level concern in... more The development of presentation attack detection (PAD) methods has become a high level concern in biometric security. As in other pattern recognition tasks, the use of deep learning is increasingly common. However, it is still doubtful if handcrafted features should be discarded. This work focused on the comparison of using handcrafted features and deep learning techniques at the feature extraction level in a face PAD method. Handcrafted features were based on Local Binary Patterns, while a Convolutional Neural Network based on VGG-16 was used for deep feature extraction. A Support Vector Machine was used for binary classification after dimensionality reduction using Principal Component Analysis. The methods were tested using the NUAA database, and the results show that handcrafted feature extraction still offer better results, with 3.1% APCER and 25.2% BPCER.
This work presents a novel multimodal database comprising 3D face, 2D face, thermal face, visible... more This work presents a novel multimodal database comprising 3D face, 2D face, thermal face, visible iris, finger and hand veins, voice and anthropometrics. This dataset will constitute a valuable resource to the field with its number and variety of biometric traits. Acquired in the context of the EU PROTECT project, the dataset allows several combinations of biometric traits and envisages applications such as border control. Based upon the results of the unimodal data, a fusion scheme was applied to ascertain the recognition potential of combining these biometric traits in a multimodal approach. Due to the variability on the discriminative power of the traits, a leave the n-best out fusion technique was applied to obtain different recognition results.
Biometrics represents a return to a traditional way of identifying someone relying on what that p... more Biometrics represents a return to a traditional way of identifying someone relying on what that person is instead of what that person knows or owns. Even though the significant amount of research that has been done in this field, there is still much to do as new emerging scenarios of application appear everyday. Biometric recognition systems are no longer restricted to forensic investigation or control management of employees. They have been gaining a visibility and applicability in daily use devices which reinforces their usability in all aspects of our day to day life. With this spread of biometric applications, nowadays commonly found in our laptops, our smart phones, some bank management services and airport custom services, a necessity for improved security also is rising. The importance of protecting our identity and our data has become crucial as our devices are filled with sensible information of many kinds. Therefore the presentation attack or liveness detection methods as ...
2020 International Conference of the Biometrics Special Interest Group (BIOSIG), 2020
Fingerprint presentation attack detection (PAD) methods present a stunning performance in current... more Fingerprint presentation attack detection (PAD) methods present a stunning performance in current literature. However, the fingerprint PAD generalisation problem is still an open challenge requiring the development of methods able to cope with sophisticated and unseen attacks as our eventual intruders become more capable. This work addresses this problem by applying a regularisation technique based on an adversarial training and representation learning specifically designed to to improve the PAD generalisation capacity of the model to an unseen attack. In the adopted approach, the model jointly learns the representation and the classifier from the data, while explicitly imposing invariance in the high-level representations regarding the type of attacks for a robust PAD. The application of the adversarial training methodology is evaluated in two different scenarios: i) a handcrafted feature extraction method combined with a Multilayer Perceptron (MLP); and ii) an end-to-end solution ...
Despite the high performance of current presentation attack detection (PAD) methods, the robustne... more Despite the high performance of current presentation attack detection (PAD) methods, the robustness to unseen attacks is still an under addressed challenge. This work approaches the problem by enforcing the learning of the bona fide presentations while making the model less dependent on the presentation attack instrument species (PAIS). The proposed model comprises an encoder, mapping from input features to latent representations, and two classifiers operating on these underlying representations: (i) the task-classifier, for predicting the class labels (as bona fide or attack); and (ii) the species-classifier, for predicting the PAIS. In the learning stage, the encoder is trained to help the task-classifier while trying to fool the species-classifier. Plus, an additional training objective enforcing the similarity of the latent distributions of different species is added leading to a ‘PAI-species’-independent model. The experimental results demonstrated that the proposed regularisat...
The spread of biometric applications in mobile devices handled by untrained users opened the door... more The spread of biometric applications in mobile devices handled by untrained users opened the door to sources of noise in mobile iris recognition such as larger extent of rotation in the capture and more off-angle imagery not found so extensively in more constrained acquisition settings. As a result of the limitations of the methods in handling such large degrees of freedom there is often an increase in segmentation errors. In this work, a new near-infrared iris dataset captured with a mobile device is evaluated to analyse, in particular, the rotation observed in images and its impact on segmentation and biometric recognition accuracy. For this study a (manually annotated) ground truth segmentation was used which will be published in tandem with the paper. Similarly to most research challenges in biometrics and computer vision in general, deep learning techniques are proving to outperform classical methods in segmentation methods. The utilization of parameterized CNN-based iris segme...
In recent years many authors have recognized that the path forward, regarding iris recognition, i... more In recent years many authors have recognized that the path forward, regarding iris recognition, is the development of iris recognition systems that can work independently of the conditions under which iris images are acquired. Recent works have tried to achieve robust and unconstrained iris recognition in order to develop real-world applicable methods. In this work, the problem of unconstrained iris recognition is referred and some results of an approach to the problem of fusing color information to enhance the performance of an iris authentication system are briefly presented.
Biometrics represents a return to a traditional way of identifying someone relying on what that p... more Biometrics represents a return to a traditional way of identifying someone relying on what that person is instead of what that person knows or owns. Even though the significant amount of research that has been done in this field, there is still much to do as new emerging scenarios of application appear everyday. Biometric recognition systems are no longer restricted to forensic investigation or control management of employees. They have been gaining a visibility and applicability in daily use devices which reinforces their usability in all aspects of our day to day life. With this spread of biometric applications, nowadays commonly found in our laptops, our smart phones, some bank management services and airport custom services, a necessity for improved security also is rising. The importance of protecting our identity and our data has become crucial as our devices are filled with sensible information of many kinds. Therefore the presentation attack or liveness detection methods as countermeasures against spoofing attacks are more important than ever. New methods should be developed which address the new acquisition scenarios and which deal with the increased noise in the biometric data collected. Its of utmost importance to develop robust liveness detection methods. In particular, we worked on iris and fingerprint. These two biometric traits are very often chosen against others due to its characteristics. Among the objectives of this thesis were the purpose of making contributions in iris and fingerprint liveness detection proposing novel approaches whether from the imaging scenarios perspective, in the case of iris, or from the classification approach, in the case of fingerprint. Contributions were made regarding both traits, that exceeded the state-of-the art and resulted in both conferences and journal publications. Not only the spoofing attacks concern the biometric researchers but also the ability of the methods to deal with the noisy data. Therefore, the development of robust methods that overcome the compromised quality of data is a necessity of biometric research of nowadays. Therefore, another objective was to contribute to the fingerprint recognition problem developing robust methods to minutiae extraction. The work developed resulted in a proposed method for fingerprint orientation map estimation and a fingerprint image enhancement that over performed existing ones. This work aimed and succeeded to propose robust and realistic methods in both the iris and fingerprint liveness detection problem as well as in some steps of fingerprint recognition. It has to be noted that the focus of attention of this work was the quality of data and not the computational efficiency, therefore this one should have to be addressed if an application of the proposed methods to a real-world scenario was aimed. Another objective was to create new databases and promote common platforms of evaluation of methods such as biometric competitions. Therefore, along the work developed, two biometric databases were constructed and two biometric competitions were organized. Both databases had a strong impact in the research community and they continue to be disseminated. Publications using these benchmark datasets are numerous and continue to appear regularly.
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