The Experts below are selected from a list of 1824 Experts worldwide ranked by ideXlab platform
Mark S. Nixon - One of the best experts on this subject based on the ideXlab platform.
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Comparative Face Soft Biometrics for Human Identification
Surveillance in Action, 2017Co-Authors: Nawaf Yousef Almudhahka, Mark S. Nixon, Jonathon S. HareAbstract:The recent growth in CCTV systems and the challenges of automatically identifying humans under the adverse visual conditions of surveillance have increased the interest in Soft Biometrics, which are physical attributes that can be used to describe people semantically. Soft Biometrics enable human identification based on verbal descriptions, and they can be captured in conditions where it is impossible to acquire traditional Biometrics such as iris and fingerprint. The research on facial Soft Biometrics has tended to focus on identification using categorical attributes, whereas comparative attributes have shown a better accuracy. Nevertheless, the research in comparative facial Soft Biometrics has been limited to small constrained databases, while identification in surveillance systems involves unconstrained large databases. In this chapter, we explore human identification through comparative facial Soft Biometrics in large unconstrained databases using the Labelled Faces in the Wild (LFW) database. We propose a novel set of attributes and investigate their significance. Also, we analyse the reliability of comparative facial Soft Biometrics for realistic databases and explore identification and verification using comparative facial Soft Biometrics. The results of the performance analysis show that by comparing an unknown subject to a line up of ten subjects only; a correct match will be found in the top 2.08% retrieved subjects from a database of 4038 subjects.
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unconstrained human identification using comparative facial Soft Biometrics
International Conference on Biometrics: Theory Applications and Systems, 2016Co-Authors: Nawaf Yousef Almudhahka, Mark S. Nixon, Jonathon S. HareAbstract:Soft Biometrics are attracting a lot of interest with the spread of surveillance systems, and the need to identify humans at distance and under adverse visual conditions. Comparative Soft Biometrics have shown a significantly better impact on identification performance compared to traditional categorical Soft Biometrics. However, existing work that has studied comparative Soft Biometrics was based on small datasets with samples taken under constrained visual conditions. In this paper, we investigate human identification using comparative facial Soft Biometrics on a larger and more realistic scale using 4038 subjects from the View 1 subset of the LFW database. Furthermore, we introduce a new set of comparative facial Soft Biometrics and investigate the effect of these on identification and verification performance. Our experiments show that by using only 24 features and 10 comparisons, a rank-10 identification rate of 96.98% and a verification accuracy of 93.66% can be achieved.
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from clothing to identity manual and automatic Soft Biometrics
IEEE Transactions on Information Forensics and Security, 2016Co-Authors: Emad Sami Jaha, Mark S. NixonAbstract:Soft Biometrics have increasingly attracted research interest and are often considered as major cues for identity, especially in the absence of valid traditional Biometrics, as in surveillance. In everyday life, several incidents and forensic scenarios highlight the usefulness and capability of identity information that can be deduced from clothing. Semantic clothing attributes have recently been introduced as a new form of Soft Biometrics. Although clothing traits can be naturally described and compared by humans for operable and successful use, it is desirable to exploit computer vision to enrich clothing descriptions with more objective and discriminative information. This allows automatic extraction and semantic description and comparison of visually detectable clothing traits in a manner similar to recognition by eyewitness statements. This paper proposes a novel set of Soft clothing attributes, described using small groups of high-level semantic labels, and automatically extracted using computer-vision techniques. In this way, we can explore the capability of human attributes vis-a-vis those which are inferred automatically by computer vision. Categorical and comparative Soft clothing traits are derived and used for identification/re-identification either to supplement Soft body traits or to be used alone. The automatically and manually derived Soft clothing Biometrics are employed in challenging invariant person retrieval. The experimental results highlight promising potential for use in various applications.
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Analysing comparative Soft Biometrics from crowdsourced annotations
IET Biometrics, 2016Co-Authors: Daniel Martinho-corbishley, Mark S. Nixon, John N. CarterAbstract:Soft Biometrics enable human description and identification from low-quality surveillance footage. This study premises the design, collection and analysis of a novel crowdsourced dataset of comparative Soft biometric body annotations, obtained from a richly diverse set of human annotators. The authors annotate 100 subject images to provide a coherent, in-depth appraisal of the collected annotations and inferred relative labels. The dataset includes gender as a comparative trait and the authors find that comparative labels characteristically contain additional discriminative information over traditional categorical annotations. Using the authors' pragmatic dataset, semantic recognition is performed by inferring relative biometric signatures using a RankSVM algorithm. This demonstrates a practical scenario, reproducing responses from a video surveillance operator searching for an individual. The approach can reliably return the correct match in the top 7% of results with ten comparisons, or top 13% of results using just five sets of subject comparisons.
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human face identification via comparative Soft Biometrics
2016 IEEE International Conference on Identity Security and Behavior Analysis (ISBA), 2016Co-Authors: Nawaf Yousef Almudhahka, Mark S. Nixon, Jonathon S. HareAbstract:Soft Biometrics enable the identification of subjects based on semantic descriptions collected from eyewitnesses allowing people to search in surveillance databases. Although research has recently shown an increased interest in Soft Biometrics, not much of the work have used crowdsourcing, and it did not investigate the impact of feature selection on identification. In this paper, we introduce a new set of facial Soft Biometrics and labels with a novel description for the eyebrow region. Also, we examine the use of crowdsourcing for labelling the comparative facial Soft Biometrics and assess its impact on the identification. Moreover, we explore the impact of feature selection with our biometric measures and evaluate the effect of label scale compression. Experiments based on the Southampton biometric tunnel database demonstrate a 100% rank-1 identification rate using 20 features only.
Sridha Sridharan - One of the best experts on this subject based on the ideXlab platform.
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identifying customer behaviour and dwell time using Soft Biometrics
Video Analytics for Business Intelligence [Studies in Computational Intelligence Volume 409], 2012Co-Authors: Simon Denman, Alina Bialkowski, Clinton Fookes, Sridha SridharanAbstract:In a commercial environment, it is advantageous to know how long it takes customers to move between different regions, how long they spend in each region, and where they are likely to go as they move from one location to another. Presently, these measures can only be determined manually, or through the use of hardware tags (i.e. RFID). Soft Biometrics are characteristics that can be used to describe, but not uniquely identify an individual. They include traits such as height, weight, gender, hair, skin and clothing colour. Unlike traditional Biometrics, Soft Biometrics can be acquired by surveillance cameras at range without any user cooperation. While these traits cannot provide robust authentication, they can be used to provide identification at long range, and aid in object tracking and detection in disjoint camera networks. In this chapter we propose using colour, height and luggage Soft Biometrics to determine operational statistics relating to how people move through a space. A novel average Soft biometric is used to locate people who look distinct, and these people are then detected at various locations within a disjoint camera network to gradually obtain operational statistics.
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Video Analytics for Business Intelligence - Identifying Customer Behaviour and Dwell Time Using Soft Biometrics
Studies in Computational Intelligence, 2012Co-Authors: Simon Denman, Alina Bialkowski, Clinton Fookes, Sridha SridharanAbstract:In a commercial environment, it is advantageous to know how long it takes customers to move between different regions, how long they spend in each region, and where they are likely to go as they move from one location to another. Presently, these measures can only be determined manually, or through the use of hardware tags (i.e. RFID). Soft Biometrics are characteristics that can be used to describe, but not uniquely identify an individual. They include traits such as height, weight, gender, hair, skin and clothing colour. Unlike traditional Biometrics, Soft Biometrics can be acquired by surveillance cameras at range without any user cooperation. While these traits cannot provide robust authentication, they can be used to provide identification at long range, and aid in object tracking and detection in disjoint camera networks. In this chapter we propose using colour, height and luggage Soft Biometrics to determine operational statistics relating to how people move through a space. A novel average Soft biometric is used to locate people who look distinct, and these people are then detected at various locations within a disjoint camera network to gradually obtain operational statistics.
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Determining operational measures from multi-camera surveillance systems using Soft Biometrics
2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance AVSS 2011, 2011Co-Authors: Simon Denman, Alina Bialkowski, Clinton Fookes, Sridha SridharanAbstract:CCTV and surveillance networks are increasingly being used for operational as well as security tasks. One emerging area of technology that lends itself to operational analytics is Soft Biometrics. Soft Biometrics can be used to describe a person and detect them throughout a sparse multi-camera network. This enables them to be used to perform tasks such as determining the time taken to get from point to point, and the paths taken through an environment by detecting and matching people across disjoint views. However, in a busy environment where there are 100's if not 1000's of people such as an airport, attempting to monitor everyone is highly unrealistic. In this paper we propose an average Soft biometric, that can be used to identity people who look distinct, and are thus suitable for monitoring through a large, sparse camera network. We demonstrate how an average Soft biometric can be used to identify unique people to calculate operational measures such as the time taken to travel from point to point.
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AVSS - Determining operational measures from multi-camera surveillance systems using Soft Biometrics
2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2011Co-Authors: Simon Denman, Alina Bialkowski, Clinton Fookes, Sridha SridharanAbstract:CCTV and surveillance networks are increasingly being used for operational as well as security tasks. One emerging area of technology that lends itself to operational analytics is Soft Biometrics. Soft Biometrics can be used to describe a person and detect them throughout a sparse multi-camera network. This enables them to be used to perform tasks such as determining the time taken to get from point to point, and the paths taken through an environment by detecting and matching people across disjoint views. However, in a busy environment where there are 100's if not 1000's of people such as an airport, attempting to monitor everyone is highly unrealistic. In this paper we propose an average Soft biometric, that can be used to identity people who look distinct, and are thus suitable for monitoring through a large, sparse camera network. We demonstrate how an average Soft biometric can be used to identify unique people to calculate operational measures such as the time taken to travel from point to point.
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Soft-Biometrics : unconstrained authentication in a surveillance environment
2010Co-Authors: Simon Denman, Alina Bialkowski, Clinton Fookes, Sridha SridharanAbstract:Soft Biometrics are characteristics that can be used to describe, but not uniquely identify an individual. These include traits such as height, weight, gender, hair, skin and clothing colour. Unlike traditional Biometrics (i.e. face, voice) which require cooperation from the subject, Soft Biometrics can be acquired by surveillance cameras at range without any user cooperation. Whilst these traits cannot provide robust authentication, they can be used to provide coarse authentication or identification at long range, locate a subject who has been previously seen or who matches a description, as well as aid in object tracking. In this paper we propose three part (head, torso, legs) height and colour Soft biometric models, and demonstrate their verification performance on a subset of the PETS 2006 database. We show that these models, whilst not as accurate as traditional Biometrics, can still achieve acceptable rates of accuracy in situations where traditional Biometrics cannot be applied.
Antitza Dantcheva - One of the best experts on this subject based on the ideXlab platform.
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what else does your biometric data reveal a survey on Soft Biometrics
IEEE Transactions on Information Forensics and Security, 2016Co-Authors: Antitza Dantcheva, Petros Elia, Arun RossAbstract:Recent research has explored the possibility of extracting ancillary information from primary biometric traits viz., face, fingerprints, hand geometry, and iris. This ancillary information includes personal attributes, such as gender, age, ethnicity, hair color, height, weight, and so on. Such attributes are known as Soft Biometrics and have applications in surveillance and indexing biometric databases. These attributes can be used in a fusion framework to improve the matching accuracy of a primary biometric system (e.g., fusing face with gender information), or can be used to generate qualitative descriptions of an individual (e.g., young Asian female with dark eyes and brown hair). The latter is particularly useful in bridging the semantic gap between human and machine descriptions of the biometric data. In this paper, we provide an overview of Soft Biometrics and discuss some of the techniques that have been proposed to extract them from the image and the video data. We also introduce a taxonomy for organizing and classifying Soft biometric attributes, and enumerate the strengths and limitations of these attributes in the context of an operational biometric system. Finally, we discuss open research problems in this field. This survey is intended for researchers and practitioners in the field of Biometrics.
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What else does your biometric data reveal? A survey on Soft Biometrics
IEEE Transactions on Information Forensics and Security, 2015Co-Authors: Antitza Dantcheva, Petros Elia, Arun RossAbstract:Recent research has explored the possibility of extracting ancillary information from primary biometric traits, viz., face, fingerprints, hand geometry and iris. This ancillary information includes personal attributes such as gender, age, ethnicity, hair color, height, weight, etc. Such attributes are known as Soft Biometrics and have applications in surveillance and indexing biometric databases. These attributes can be used in a fusion framework to improve the matching accuracy of a primary biometric system (e.g., fusing face with gender information), or can be used to generate qualitative descriptions of an individual (e.g., "young Asian female with dark eyes and brown hair"). The latter is particularly useful in bridging the semantic gap between human and machine descriptions of biometric data. In this paper, we provide an overview of Soft Biometrics and discuss some of the techniques that have been proposed to extract them from image and video data. We also introduce a taxonomy for organizing and classifying Soft biometric attributes, and enumerate the strengths and limitations of these attributes in the context of an operational biometric system. Finally, we discuss open research problems in this field. This survey is intended for researchers and practitioners in the field of Biometrics.
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Facial Soft Biometrics : Methods, applications, and solutions
2011Co-Authors: Antitza DantchevaAbstract:This dissertation studies Soft Biometrics traits, their applicability in different security and commercial scenarios, as well as related usability aspects. We place the emphasis on human facial Soft biometric traits which constitute the set of physical, adhered or behavioral human characteristics that can partially differentiate, classify and identify humans. Such traits, which include characteristics like age, gender, hair, skin and eye color, the presence of glasses, moustache or beard, inherit several advantages such as ease of acquisition, as well as a natural compatibility with how humans perceive their surroundings. Specifically, Soft biometric traits are compatible with the human process of classifying and recalling our environment, a process which involves constructions of hierarchical structures of different refined traits. This thesis explores these traits, and their application in Soft biometric systems (SBSs), and specifically focuses on how such systems can achieve different goals including database search pruning, human identification, human re-identification and, on a different note, prediction and quantification of facial aesthetics. Our motivation originates from the emerging importance of such applications in our evolving society, as well as from the practicality of such systems. SBSs generally benefit from the non-intrusive nature of acquiring Soft biometric traits, and enjoy computational efficiency which in turn allows for fast, enrolment-free and pose-flexible biometric analysis, even in the absence consent and cooperation by the involved human subjects. These benefits render Soft Biometrics indispensable in applications that involve processing of real life images and videos.
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Bag of Soft Biometrics for person identification
Multimedia Tools and Applications, 2011Co-Authors: Antitza Dantcheva, Angela D’angelo, Carmelo Velardo, Jean-luc DugelayAbstract:In this work we seek to provide insight on the general topic of Soft Biometrics. We firstly present a new refined definition of Soft Biometrics, emphasizing on the aspect of human compliance, and then proceed to identify candidate traits that accept this novel definition. We then address relations between traits and discuss associated benefits and limitations of these traits. We also consider two novel Soft biometric traits, namely weight and color of clothes and we analyze their reliability. Related promising results on the performance are provided. Finally, we consider a new application, namely human identification solely carried out by a bag of facial, body and accessory Soft biometric traits, and as an evidence of its practicality, we provide preliminary promising results.
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Female facial aesthetics based on Soft Biometrics and photo-quality
2011Co-Authors: Antitza DantchevaAbstract:In this work we study the connection between subjective evaluation of facial aesthetics and selected objective parameters based on photo-quality and facial Soft Biometrics. The approach is novel in that it jointly considers both previous results on photo quality and beauty assessment, as well as it incorporates non-permanent facial characteristics and expressions in the context of female facial aesthetics. This study helps us understand the role of this specific set of features in affecting the way humans perceive facial images. Based on the above objective parameters, we further construct a simple linear metric that hints modifiable parameters for aesthetics enhancement, as well as tunes Soft biometric systems that would seek to predict the way humans perceive facial aesthetics.
Jonathon S. Hare - One of the best experts on this subject based on the ideXlab platform.
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Comparative Face Soft Biometrics for Human Identification
Surveillance in Action, 2017Co-Authors: Nawaf Yousef Almudhahka, Mark S. Nixon, Jonathon S. HareAbstract:The recent growth in CCTV systems and the challenges of automatically identifying humans under the adverse visual conditions of surveillance have increased the interest in Soft Biometrics, which are physical attributes that can be used to describe people semantically. Soft Biometrics enable human identification based on verbal descriptions, and they can be captured in conditions where it is impossible to acquire traditional Biometrics such as iris and fingerprint. The research on facial Soft Biometrics has tended to focus on identification using categorical attributes, whereas comparative attributes have shown a better accuracy. Nevertheless, the research in comparative facial Soft Biometrics has been limited to small constrained databases, while identification in surveillance systems involves unconstrained large databases. In this chapter, we explore human identification through comparative facial Soft Biometrics in large unconstrained databases using the Labelled Faces in the Wild (LFW) database. We propose a novel set of attributes and investigate their significance. Also, we analyse the reliability of comparative facial Soft Biometrics for realistic databases and explore identification and verification using comparative facial Soft Biometrics. The results of the performance analysis show that by comparing an unknown subject to a line up of ten subjects only; a correct match will be found in the top 2.08% retrieved subjects from a database of 4038 subjects.
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unconstrained human identification using comparative facial Soft Biometrics
International Conference on Biometrics: Theory Applications and Systems, 2016Co-Authors: Nawaf Yousef Almudhahka, Mark S. Nixon, Jonathon S. HareAbstract:Soft Biometrics are attracting a lot of interest with the spread of surveillance systems, and the need to identify humans at distance and under adverse visual conditions. Comparative Soft Biometrics have shown a significantly better impact on identification performance compared to traditional categorical Soft Biometrics. However, existing work that has studied comparative Soft Biometrics was based on small datasets with samples taken under constrained visual conditions. In this paper, we investigate human identification using comparative facial Soft Biometrics on a larger and more realistic scale using 4038 subjects from the View 1 subset of the LFW database. Furthermore, we introduce a new set of comparative facial Soft Biometrics and investigate the effect of these on identification and verification performance. Our experiments show that by using only 24 features and 10 comparisons, a rank-10 identification rate of 96.98% and a verification accuracy of 93.66% can be achieved.
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human face identification via comparative Soft Biometrics
2016 IEEE International Conference on Identity Security and Behavior Analysis (ISBA), 2016Co-Authors: Nawaf Yousef Almudhahka, Mark S. Nixon, Jonathon S. HareAbstract:Soft Biometrics enable the identification of subjects based on semantic descriptions collected from eyewitnesses allowing people to search in surveillance databases. Although research has recently shown an increased interest in Soft Biometrics, not much of the work have used crowdsourcing, and it did not investigate the impact of feature selection on identification. In this paper, we introduce a new set of facial Soft Biometrics and labels with a novel description for the eyebrow region. Also, we examine the use of crowdsourcing for labelling the comparative facial Soft Biometrics and assess its impact on the identification. Moreover, we explore the impact of feature selection with our biometric measures and evaluate the effect of label scale compression. Experiments based on the Southampton biometric tunnel database demonstrate a 100% rank-1 identification rate using 20 features only.
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BTAS - Unconstrained human identification using comparative facial Soft Biometrics
2016 IEEE 8th International Conference on Biometrics Theory Applications and Systems (BTAS), 2016Co-Authors: Nawaf Yousef Almudhahka, Mark S. Nixon, Jonathon S. HareAbstract:Soft Biometrics are attracting a lot of interest with the spread of surveillance systems, and the need to identify humans at distance and under adverse visual conditions. Comparative Soft Biometrics have shown a significantly better impact on identification performance compared to traditional categorical Soft Biometrics. However, existing work that has studied comparative Soft Biometrics was based on small datasets with samples taken under constrained visual conditions. In this paper, we investigate human identification using comparative facial Soft Biometrics on a larger and more realistic scale using 4038 subjects from the View 1 subset of the LFW database. Furthermore, we introduce a new set of comparative facial Soft Biometrics and investigate the effect of these on identification and verification performance. Our experiments show that by using only 24 features and 10 comparisons, a rank-10 identification rate of 96.98% and a verification accuracy of 93.66% can be achieved.
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ISBA - Human face identification via comparative Soft Biometrics
2016 IEEE International Conference on Identity Security and Behavior Analysis (ISBA), 2016Co-Authors: Nawaf Yousef Almudhahka, Mark S. Nixon, Jonathon S. HareAbstract:Soft Biometrics enable the identification of subjects based on semantic descriptions collected from eyewitnesses allowing people to search in surveillance databases. Although research has recently shown an increased interest in Soft Biometrics, not much of the work have used crowdsourcing, and it did not investigate the impact of feature selection on identification. In this paper, we introduce a new set of facial Soft Biometrics and labels with a novel description for the eyebrow region. Also, we examine the use of crowdsourcing for labelling the comparative facial Soft Biometrics and assess its impact on the identification. Moreover, we explore the impact of feature selection with our biometric measures and evaluate the effect of label scale compression. Experiments based on the Southampton biometric tunnel database demonstrate a 100% rank-1 identification rate using 20 features only.
Simon Denman - One of the best experts on this subject based on the ideXlab platform.
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identifying customer behaviour and dwell time using Soft Biometrics
Video Analytics for Business Intelligence [Studies in Computational Intelligence Volume 409], 2012Co-Authors: Simon Denman, Alina Bialkowski, Clinton Fookes, Sridha SridharanAbstract:In a commercial environment, it is advantageous to know how long it takes customers to move between different regions, how long they spend in each region, and where they are likely to go as they move from one location to another. Presently, these measures can only be determined manually, or through the use of hardware tags (i.e. RFID). Soft Biometrics are characteristics that can be used to describe, but not uniquely identify an individual. They include traits such as height, weight, gender, hair, skin and clothing colour. Unlike traditional Biometrics, Soft Biometrics can be acquired by surveillance cameras at range without any user cooperation. While these traits cannot provide robust authentication, they can be used to provide identification at long range, and aid in object tracking and detection in disjoint camera networks. In this chapter we propose using colour, height and luggage Soft Biometrics to determine operational statistics relating to how people move through a space. A novel average Soft biometric is used to locate people who look distinct, and these people are then detected at various locations within a disjoint camera network to gradually obtain operational statistics.
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Video Analytics for Business Intelligence - Identifying Customer Behaviour and Dwell Time Using Soft Biometrics
Studies in Computational Intelligence, 2012Co-Authors: Simon Denman, Alina Bialkowski, Clinton Fookes, Sridha SridharanAbstract:In a commercial environment, it is advantageous to know how long it takes customers to move between different regions, how long they spend in each region, and where they are likely to go as they move from one location to another. Presently, these measures can only be determined manually, or through the use of hardware tags (i.e. RFID). Soft Biometrics are characteristics that can be used to describe, but not uniquely identify an individual. They include traits such as height, weight, gender, hair, skin and clothing colour. Unlike traditional Biometrics, Soft Biometrics can be acquired by surveillance cameras at range without any user cooperation. While these traits cannot provide robust authentication, they can be used to provide identification at long range, and aid in object tracking and detection in disjoint camera networks. In this chapter we propose using colour, height and luggage Soft Biometrics to determine operational statistics relating to how people move through a space. A novel average Soft biometric is used to locate people who look distinct, and these people are then detected at various locations within a disjoint camera network to gradually obtain operational statistics.
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Determining operational measures from multi-camera surveillance systems using Soft Biometrics
2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance AVSS 2011, 2011Co-Authors: Simon Denman, Alina Bialkowski, Clinton Fookes, Sridha SridharanAbstract:CCTV and surveillance networks are increasingly being used for operational as well as security tasks. One emerging area of technology that lends itself to operational analytics is Soft Biometrics. Soft Biometrics can be used to describe a person and detect them throughout a sparse multi-camera network. This enables them to be used to perform tasks such as determining the time taken to get from point to point, and the paths taken through an environment by detecting and matching people across disjoint views. However, in a busy environment where there are 100's if not 1000's of people such as an airport, attempting to monitor everyone is highly unrealistic. In this paper we propose an average Soft biometric, that can be used to identity people who look distinct, and are thus suitable for monitoring through a large, sparse camera network. We demonstrate how an average Soft biometric can be used to identify unique people to calculate operational measures such as the time taken to travel from point to point.
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AVSS - Determining operational measures from multi-camera surveillance systems using Soft Biometrics
2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2011Co-Authors: Simon Denman, Alina Bialkowski, Clinton Fookes, Sridha SridharanAbstract:CCTV and surveillance networks are increasingly being used for operational as well as security tasks. One emerging area of technology that lends itself to operational analytics is Soft Biometrics. Soft Biometrics can be used to describe a person and detect them throughout a sparse multi-camera network. This enables them to be used to perform tasks such as determining the time taken to get from point to point, and the paths taken through an environment by detecting and matching people across disjoint views. However, in a busy environment where there are 100's if not 1000's of people such as an airport, attempting to monitor everyone is highly unrealistic. In this paper we propose an average Soft biometric, that can be used to identity people who look distinct, and are thus suitable for monitoring through a large, sparse camera network. We demonstrate how an average Soft biometric can be used to identify unique people to calculate operational measures such as the time taken to travel from point to point.
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Soft-Biometrics : unconstrained authentication in a surveillance environment
2010Co-Authors: Simon Denman, Alina Bialkowski, Clinton Fookes, Sridha SridharanAbstract:Soft Biometrics are characteristics that can be used to describe, but not uniquely identify an individual. These include traits such as height, weight, gender, hair, skin and clothing colour. Unlike traditional Biometrics (i.e. face, voice) which require cooperation from the subject, Soft Biometrics can be acquired by surveillance cameras at range without any user cooperation. Whilst these traits cannot provide robust authentication, they can be used to provide coarse authentication or identification at long range, locate a subject who has been previously seen or who matches a description, as well as aid in object tracking. In this paper we propose three part (head, torso, legs) height and colour Soft biometric models, and demonstrate their verification performance on a subset of the PETS 2006 database. We show that these models, whilst not as accurate as traditional Biometrics, can still achieve acceptable rates of accuracy in situations where traditional Biometrics cannot be applied.