Guide to Biometric Reference Systems and Performance Evaluation, 2009
Abstract An overview of selected topics in face recognition is first presented in this chapter. T... more Abstract An overview of selected topics in face recognition is first presented in this chapter. The BioSecure 2D-face Benchmarking Framework is also described, com-posed of open-source software, publicly available databases and protocols. Three methods for 2D-face ...
Faces are highly deformable objects which may easily change their appearance over time. Not all f... more Faces are highly deformable objects which may easily change their appearance over time. Not all face areas are subject to the same variability. Therefore decoupling the information from independent areas of the face is of paramount importance to improve the robustness of any face recognition technique. This paper presents a robust face recognition technique based on the extraction and matching of SIFT features related to independent face areas. Both a global and local (as recognition from parts) matching strategy is proposed. The local strategy is based on matching individual salient facial SIFT features as connected to facial landmarks such as the eyes and the mouth. As for the global matching strategy, all SIFT features are combined together to form a single feature. In order to reduce the identification errors, the Dempster-Shafer decision theory is applied to fuse the two matching techniques. The proposed algorithms are evaluated with the ORL and the IITK face databases. The exp...
In the last decade deep learning techniques have strongly influenced many aspects of computationa... more In the last decade deep learning techniques have strongly influenced many aspects of computational vision. Many difficult vision tasks can now be performed by deploying a properly tailored and trained deep network. The enthusiasm for deep learning is unfortunately paired by the present lack of a clear understanding of how they work and why they provide such brilliant performance. The same applies to biometric systems. Deep learning has been successfully applied to several biometric recognition tasks, including face recognition. VGG-face is possibly the first deep convolutional network designed to perform face recognition, obtaining unsurpassed performance at the time it was firstly proposed. Over the last years, several and more complex deep convolutional networks, trained on very large, mainly private, datasets, have been proposed still elevating the performance bar also on quite challenging public databases, such as the Janus IJB-A and IJB-B. Despite of the progress in the develop...
The editorial briefs the selected articles under the IEEE Access special section entitled “Visual... more The editorial briefs the selected articles under the IEEE Access special section entitled “Visual Surveillance and Biometrics: Practices, Challenges, and Possibilities”. INDEX TERMS Biometrics, Visual Surveillance, Forensics.
Recent research on face analysis has demonstrated the richness of information embedded in feature... more Recent research on face analysis has demonstrated the richness of information embedded in feature vectors extracted from a deep convolutional neural network. Even though deep learning achieved a very high performance on several challenging visual tasks, such as determining the identity, age, gender and race, it still lacks a well grounded theory which allows to properly understand the processes taking place inside the network layers. Therefore, most of the underlying processes are unknown and not easy to control. On the other hand, the human visual system follows a well understood process in analyzing a scene or an object, such as a face. The direction of the eye gaze is repeatedly directed, through purposively planned saccadic movements, towards salient regions to capture several details. In this paper we propose to capitalize on the knowledge of the saccadic human visual processes to design a system to predict facial attributes embedding a biologically-inspired network architectur...
In everyday life whenever people observe, interact or speak to each other, visual attention is mo... more In everyday life whenever people observe, interact or speak to each other, visual attention is mostly directed toward the other person’s face, particularly to the eyes and the nearby periocular regions. This is naturally reflected when the user interacts with their mobile phones in several usual activities, such as web access, payments and video calls. For this reason, the functionality of mobile devices is strongly affected by the design of the user interface. In this chapter, we propose a biologically inspired approach for continuous user authentication based on the analysis of the ocular regions. The proposed system is based on a modified version of the HMAX visual processing module. HMAX is a hierarchical model which has been conceived to mimic the basic neural architecture of the ventral stream of the visual cortex. The original HMAX model consists of four layers: S1, C1, S2 and C2. S1 and C1 represent the responses to a bank of orientation-selective Gabor filters. S2 and C2 represent the responses of simple and complex cells to other textural features. The discrimination power of HMAX in recognizing classes of objects is invariant to rotation and scale. The C1 layer, which is mainly responsible for the scale and rotation invariance, is implemented using a max-pooling operation, which may lose some spatial information. To overcome this problem while preserving the maximal visual acuity and hence the localization accuracy, we propose to augment the model by applying a retinal log-polar mapping. The log-polar mapping is an approximation of the retino-cortical mapping that is performed by the early stages of the primate visual system. Due to the high density of the cones in the fovea, the log-polar approximation of the space-variant distribution model of the photoreceptors can only be applied outside the foveal region. Therefore, the log-polar mapping is added to the HMAX model as a complementary stage to process the peripheral region of the grabbed images. In order to demonstrate the feasibility of the proposed approach to mobile scenarios, experimental results obtained from publicly available databases and image streams grabbed from mobile devices will be presented.
Face Recognition Across the Imaging Spectrum, 2016
Over the last decade, performance of face recognition algorithms systematically improved. This is... more Over the last decade, performance of face recognition algorithms systematically improved. This is particularly impressive when considering very large or challenging datasets such as the FRGC v2 or Labelled Faces in the Wild . A better analysis of the structure of the facial texture and shape is one of the main reasons of improvement in recognition performance. Hybrid face recognition methods , combining holistic and feature-based approaches, also allowed to increase efficiency and robustness. Both photometric information and shape information allow to extract facial features which can be exploited for recognition. However, both sources, grey levels of image pixels and 3D data , are affected by several noise sources which may impair the recognition performance. One of the main difficulties in matching 3D faces is the detection and localization of distinctive and stable points in 3D scans. Moreover, the large amount of data (tens of thousands of points) to be processed make the direct one-to-one matching a very time-consuming process. On the other hand, matching algorithms based on the analysis of 2D data alone are very sensitive to variations in illumination, expression and pose. Algorithms, based on the face shape information alone, are instead relatively insensitive to these sources of noise. These mutually exclusive features of 2D- and 3D-based face recognition algorithm call for a cooperative scheme which may take advantage of the strengths of both, while coping for their weaknesses. We envisage many real and practical applications where 2D data can be used to improve 3D matching and vice versa. Towards this end, this chapter highlights both the advantages and disadvantages of 2D- and 3D-based face recognition algorithms . It also explores the advantages of blending 2D- and 3D data -based techniques, also proposing a novel approach for a fast and robust matching. Several experimental results, obtained from publicly available datasets, currently at the state of the art, demonstrate the effectiveness of the proposed approach.
Cancellable biometrics (CB) intentionally distorts biometric template for security protection, an... more Cancellable biometrics (CB) intentionally distorts biometric template for security protection, and simultaneously preserving the distance/similarity for matching in the transformed domain. Despite its effectiveness, the security issues attributed to similarity preservation property of CB is underestimated. Dong et al. [BTAS'19], exploited the similarity preservation trait of CB and proposed a similarity-based attack with high successful attack rate. The similarity-based attack utilizes preimage that generated from the protected biometric template for impersonation and perform cross matching. In this paper, we propose a constrained optimization similarity-based attack (CSA), which is improved upon Dong's genetic algorithm enabled similarity-based attack (GASA). The CSA applies algorithm-specific equality or inequality relations as constraints, to optimize preimage generation. We justify the effectiveness of CSA from the supervised learning perspective. We conduct extensive ex...
Guide to Biometric Reference Systems and Performance Evaluation, 2009
Abstract An overview of selected topics in face recognition is first presented in this chapter. T... more Abstract An overview of selected topics in face recognition is first presented in this chapter. The BioSecure 2D-face Benchmarking Framework is also described, com-posed of open-source software, publicly available databases and protocols. Three methods for 2D-face ...
Faces are highly deformable objects which may easily change their appearance over time. Not all f... more Faces are highly deformable objects which may easily change their appearance over time. Not all face areas are subject to the same variability. Therefore decoupling the information from independent areas of the face is of paramount importance to improve the robustness of any face recognition technique. This paper presents a robust face recognition technique based on the extraction and matching of SIFT features related to independent face areas. Both a global and local (as recognition from parts) matching strategy is proposed. The local strategy is based on matching individual salient facial SIFT features as connected to facial landmarks such as the eyes and the mouth. As for the global matching strategy, all SIFT features are combined together to form a single feature. In order to reduce the identification errors, the Dempster-Shafer decision theory is applied to fuse the two matching techniques. The proposed algorithms are evaluated with the ORL and the IITK face databases. The exp...
In the last decade deep learning techniques have strongly influenced many aspects of computationa... more In the last decade deep learning techniques have strongly influenced many aspects of computational vision. Many difficult vision tasks can now be performed by deploying a properly tailored and trained deep network. The enthusiasm for deep learning is unfortunately paired by the present lack of a clear understanding of how they work and why they provide such brilliant performance. The same applies to biometric systems. Deep learning has been successfully applied to several biometric recognition tasks, including face recognition. VGG-face is possibly the first deep convolutional network designed to perform face recognition, obtaining unsurpassed performance at the time it was firstly proposed. Over the last years, several and more complex deep convolutional networks, trained on very large, mainly private, datasets, have been proposed still elevating the performance bar also on quite challenging public databases, such as the Janus IJB-A and IJB-B. Despite of the progress in the develop...
The editorial briefs the selected articles under the IEEE Access special section entitled “Visual... more The editorial briefs the selected articles under the IEEE Access special section entitled “Visual Surveillance and Biometrics: Practices, Challenges, and Possibilities”. INDEX TERMS Biometrics, Visual Surveillance, Forensics.
Recent research on face analysis has demonstrated the richness of information embedded in feature... more Recent research on face analysis has demonstrated the richness of information embedded in feature vectors extracted from a deep convolutional neural network. Even though deep learning achieved a very high performance on several challenging visual tasks, such as determining the identity, age, gender and race, it still lacks a well grounded theory which allows to properly understand the processes taking place inside the network layers. Therefore, most of the underlying processes are unknown and not easy to control. On the other hand, the human visual system follows a well understood process in analyzing a scene or an object, such as a face. The direction of the eye gaze is repeatedly directed, through purposively planned saccadic movements, towards salient regions to capture several details. In this paper we propose to capitalize on the knowledge of the saccadic human visual processes to design a system to predict facial attributes embedding a biologically-inspired network architectur...
In everyday life whenever people observe, interact or speak to each other, visual attention is mo... more In everyday life whenever people observe, interact or speak to each other, visual attention is mostly directed toward the other person’s face, particularly to the eyes and the nearby periocular regions. This is naturally reflected when the user interacts with their mobile phones in several usual activities, such as web access, payments and video calls. For this reason, the functionality of mobile devices is strongly affected by the design of the user interface. In this chapter, we propose a biologically inspired approach for continuous user authentication based on the analysis of the ocular regions. The proposed system is based on a modified version of the HMAX visual processing module. HMAX is a hierarchical model which has been conceived to mimic the basic neural architecture of the ventral stream of the visual cortex. The original HMAX model consists of four layers: S1, C1, S2 and C2. S1 and C1 represent the responses to a bank of orientation-selective Gabor filters. S2 and C2 represent the responses of simple and complex cells to other textural features. The discrimination power of HMAX in recognizing classes of objects is invariant to rotation and scale. The C1 layer, which is mainly responsible for the scale and rotation invariance, is implemented using a max-pooling operation, which may lose some spatial information. To overcome this problem while preserving the maximal visual acuity and hence the localization accuracy, we propose to augment the model by applying a retinal log-polar mapping. The log-polar mapping is an approximation of the retino-cortical mapping that is performed by the early stages of the primate visual system. Due to the high density of the cones in the fovea, the log-polar approximation of the space-variant distribution model of the photoreceptors can only be applied outside the foveal region. Therefore, the log-polar mapping is added to the HMAX model as a complementary stage to process the peripheral region of the grabbed images. In order to demonstrate the feasibility of the proposed approach to mobile scenarios, experimental results obtained from publicly available databases and image streams grabbed from mobile devices will be presented.
Face Recognition Across the Imaging Spectrum, 2016
Over the last decade, performance of face recognition algorithms systematically improved. This is... more Over the last decade, performance of face recognition algorithms systematically improved. This is particularly impressive when considering very large or challenging datasets such as the FRGC v2 or Labelled Faces in the Wild . A better analysis of the structure of the facial texture and shape is one of the main reasons of improvement in recognition performance. Hybrid face recognition methods , combining holistic and feature-based approaches, also allowed to increase efficiency and robustness. Both photometric information and shape information allow to extract facial features which can be exploited for recognition. However, both sources, grey levels of image pixels and 3D data , are affected by several noise sources which may impair the recognition performance. One of the main difficulties in matching 3D faces is the detection and localization of distinctive and stable points in 3D scans. Moreover, the large amount of data (tens of thousands of points) to be processed make the direct one-to-one matching a very time-consuming process. On the other hand, matching algorithms based on the analysis of 2D data alone are very sensitive to variations in illumination, expression and pose. Algorithms, based on the face shape information alone, are instead relatively insensitive to these sources of noise. These mutually exclusive features of 2D- and 3D-based face recognition algorithm call for a cooperative scheme which may take advantage of the strengths of both, while coping for their weaknesses. We envisage many real and practical applications where 2D data can be used to improve 3D matching and vice versa. Towards this end, this chapter highlights both the advantages and disadvantages of 2D- and 3D-based face recognition algorithms . It also explores the advantages of blending 2D- and 3D data -based techniques, also proposing a novel approach for a fast and robust matching. Several experimental results, obtained from publicly available datasets, currently at the state of the art, demonstrate the effectiveness of the proposed approach.
Cancellable biometrics (CB) intentionally distorts biometric template for security protection, an... more Cancellable biometrics (CB) intentionally distorts biometric template for security protection, and simultaneously preserving the distance/similarity for matching in the transformed domain. Despite its effectiveness, the security issues attributed to similarity preservation property of CB is underestimated. Dong et al. [BTAS'19], exploited the similarity preservation trait of CB and proposed a similarity-based attack with high successful attack rate. The similarity-based attack utilizes preimage that generated from the protected biometric template for impersonation and perform cross matching. In this paper, we propose a constrained optimization similarity-based attack (CSA), which is improved upon Dong's genetic algorithm enabled similarity-based attack (GASA). The CSA applies algorithm-specific equality or inequality relations as constraints, to optimize preimage generation. We justify the effectiveness of CSA from the supervised learning perspective. We conduct extensive ex...
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