The Experts below are selected from a list of 360 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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Soft Biometric Fusion for Subject Recognition at a Distance
IEEE Transactions on Biometrics Behavior and Identity Science, 2019Co-Authors: Mark S. Nixon, John N. CarterAbstract:There is societal need for techniques to identify subjects at a distance and when conventional Biometrics are obscured, for example in fighting crime. Soft Biometrics have this capability and include a subject's height, weight, skin colour and gender. Although the distinctiveness of soft Biometric features is intuitively less than that of traditional Biometric features, numerous experiments have demonstrated that the desired recognition accuracy can be achieved by using multiple soft Biometric features. This paper will propose state-of-the-art multimodal Biometric fusion techniques to improve recognition performance of soft Biometrics. The key contribution of this paper is the analysis of the influence of distance on soft Biometric traits and an exploration of the potency of recognition using fusion at varying distances. A new soft Biometric database, containing images of the human face, body and clothing taken at three different distances, was created and used to obtain face, body and clothing attributes. This new database was constructed to explore the suitability of each modality at a distance: intuitively, the face is suitable for near field identification, and the body becomes the optimal choice when the subject is further away. The new dataset is used to explore the potential of face, body and clothing for human recognition using fusion. We present a novel fusion technique at score and rank level that improves identification performance. A novel joint density distribution-based rank-score fusion is also proposed to combine rank and score information. Analysis using the new soft Biometric database demonstrates that recognition performance is significantly improved by using the new methods over single modalities at different distances.
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A Joint Density Based Rank-Score Fusion for Soft Biometric Recognition at a Distance
2018 24th International Conference on Pattern Recognition (ICPR), 2018Co-Authors: Mark S. Nixon, John N. CarterAbstract:In order to improve recognition performance, fusion has become a key technique in the recent years. Compared with single-mode Biometrics, the recognition rate of multi-modal Biometric systems is improved and the final decision is more confident. This paper introduces a novel joint density distribution based rank-score fusion strategy that combines rank and score information. Recognition at a distance has only recently been of interest in soft Biometrics. We create a new soft Biometric database containing the human face, body and clothing attributes at three different distances to investigate the influence by distance on soft Biometric fusion. A comparative study about our method and other state of the art rank level and score level fusion methods are also conducted in this paper. The experiments are performed using a soft Biometric database we created. The results demonstrate the recognition performance is significantly improved by our proposed method.
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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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Soft Biometrics; Human Identification Using Comparative Descriptions
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014Co-Authors: Daniel A. Reid, Mark S. Nixon, Sarah V. StevenageAbstract:Soft Biometrics are a new form of Biometric identification which use physical or behavioral traits that can be naturally described by humans. Unlike other Biometric approaches, this allows identification based solely on verbal descriptions, bridging the semantic gap between Biometrics and human description. To permit soft Biometric identification the description must be accurate, yet conventional human descriptions comprising of absolute labels and estimations are often unreliable. A novel method of obtaining human descriptions will be introduced which utilizes comparative categorical labels to describe differences between subjects. This innovative approach has been shown to address many problems associated with absolute categorical labels-most critically, the descriptions contain more objective information and have increased discriminatory capabilities. Relative measurements of the subjects' traits can be inferred from comparative human descriptions using the Elo rating system. The resulting soft Biometric signatures have been demonstrated to be robust and allow accurate recognition of subjects. Relative measurements can also be obtained from other forms of human representation. This is demonstrated using a support vector machine to determine relative measurements from gait Biometric signatures-allowing retrieval of subjects from video footage by using human comparisons, bridging the semantic gap.
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keynote lecture 3 on gait and soft Biometrics for surveillance
Advanced Video and Signal Based Surveillance, 2013Co-Authors: Mark S. NixonAbstract:Summary form only given. The prime advantage of gait as a Biometric is that it can be used for recognition at a distance whereas other Biometrics cannot. There is a rich selection of approaches and many advances have been made, as will be reviewed in this talk. Soft Biometrics is an emerging area of interest in Biometrics where we augment computer vision derived measures by human descriptions. Applied to gait Biometrics, this again can be used where other Biometric data is obscured or at too low resolution. The human descriptions are semantic and are a set of labels which are converted into numbers. Naturally, there are considerations of language and psychology when the labels are collected. After describing current progress in gait Biometrics, this talk will describe how the soft Biometrics labels are collected, and how they can be used to enhance recognising people by the way they walk. As well as reinforcing Biometrics, this approach might lead to a new procedure for collecting witness statements, and to the ability to retrieve subjects from video using witness statements.
C.v. Jawahar - One of the best experts on this subject based on the ideXlab platform.
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Blind Authentication: A Secure Crypto-Biometric Verification Protocol
IEEE Transactions on Information Forensics and Security, 2010Co-Authors: Maneesh Upmanyu, Kannan Srinathan, Aryan M A Namboodiri, C.v. JawaharAbstract:Concerns on widespread use of Biometric authentication systems are primarily centered around template security, revocability, and privacy. The use of cryptographic primitives to bolster the authentication process can alleviate some of these concerns as shown by Biometric cryptosystems. In this paper, we propose a provably secure and blind Biometric authentication protocol, which addresses the concerns of user's privacy, template protection, and trust issues. The protocol is blind in the sense that it reveals only the identity, and no additional information about the user or the Biometric to the authenticating server or vice-versa. As the protocol is based on asymmetric encryption of the Biometric data, it captures the advantages of Biometric authentication as well as the security of public key cryptography. The authentication protocol can run over public networks and provide nonrepudiable identity verification. The encryption also provides template protection, the ability to revoke enrolled templates, and alleviates the concerns on privacy in widespread use of Biometrics. The proposed approach makes no restrictive assumptions on the Biometric data and is hence applicable to multiple Biometrics. Such a protocol has significant advantages over existing Biometric cryptosystems, which use a Biometric to secure a secret key, which in turn is used for authentication. We analyze the security of the protocol under various attack scenarios. Experimental results on four Biometric datasets (face, iris, hand geometry, and fingerprint) show that carrying out the authentication in the encrypted domain does not affect the accuracy, while the encryption key acts as an additional layer of security.
Arun Ross - 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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bridging the gap from Biometrics to forensics
Philosophical Transactions of the Royal Society B, 2015Co-Authors: Anil K. Jain, Arun RossAbstract:Biometric recognition, or simply Biometrics, refers to automated recognition of individuals based on their behavioural and biological characteristics. The success of fingerprints in forensic science and law enforcement applications, coupled with growing concerns related to border control, financial fraud and cyber security, has generated a huge interest in using fingerprints, as well as other biological traits, for automated person recognition. It is, therefore, not surprising to see Biometrics permeating various segments of our society. Applications include smartphone security, mobile payment, border crossing, national civil registry and access to restricted facilities. Despite these successful deployments in various fields, there are several existing challenges and new opportunities for person recognition using Biometrics. In particular, when Biometric data is acquired in an unconstrained environment or if the subject is uncooperative, the quality of the ensuing Biometric data may not be amenable for automated person recognition. This is particularly true in crime-scene investigations, where the biological evidence gleaned from a scene may be of poor quality. In this article, we first discuss how Biometrics evolved from forensic science and how its focus is shifting back to its origin in order to address some challenging problems. Next, we enumerate the similarities and differences between Biometrics and forensics. We then present some applications where the principles of Biometrics are being successfully leveraged into forensics in order to solve critical problems in the law enforcement domain. Finally, we discuss new collaborative opportunities for researchers in Biometrics and forensics, in order to address hitherto unsolved problems that can benefit society at large.
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soft Biometrics for surveillance an overview
Handbook of Statistics, 2013Co-Authors: Daniel A. Reid, Mark S. Nixon, Sina Samangooei, Cunjian Chen, Arun RossAbstract:Abstract Biometrics is the science of automatically recognizing people based on physical or behavioral characteristics such as face, fingerprint, iris, hand, voice, gait, and signature. More recently, the use of soft Biometric traits has been proposed to improve the performance of traditional Biometric systems and allow identification based on human descriptions. Soft Biometric traits include characteristics such as height, weight, body geometry, scars, marks, and tattoos (SMT), gender, etc. These traits offer several advantages over traditional Biometric techniques. Soft Biometric traits can be typically described using human understandable labels and measurements, allowing for retrieval and recognition solely based on verbal descriptions. Unlike many primary Biometric traits, soft Biometrics can be obtained at a distance without subject cooperation and from low quality video footage, making them ideal for use in surveillance applications. This chapter will introduce the current state of the art in the emerging field of soft Biometrics.
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handbook of Biometrics
2007Co-Authors: Anil K. Jain, Patrick J Flynn, Arun RossAbstract:Biometrics is a rapidly evolving field with applications ranging from accessing ones computer to gaining entry into a country. The deployment of large-scale Biometric systems in both commercial and government applications has increased public awareness of this technology. Recent years have seen significant growth in Biometric research resulting in the development of innovative sensors, new algorithms, enhanced test methodologies and novel applications. This book addresses this void by inviting some of the prominent researchers in Biometrics to contribute chapters describing the fundamentals as well as the latest innovations in their respective areas of expertise.
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Biometrics a grand challenge
International Conference on Pattern Recognition, 2004Co-Authors: Anil K. Jain, Salil Prabhakar, Sharath Pankanti, Lin Hong, Arun RossAbstract:Reliable person identification is an important problem in diverse businesses. Biometrics, identification based on distinctive personal traits, has the potential to become an irreplaceable part of any identification system. While successful in some niche markets, the Biometrics technology has not yet delivered its promise of foolproof automatic identification. With the availability of inexpensive Biometric sensors and computing power, it is becoming increasingly clear that widespread usage of Biometric person identification is being stymied by our lack of understanding of three fundamental problems; (i) How to accurately and efficiently represent and recognize Biometric patterns? (ii) How to guarantee that the sensed measurements are not fraudulent? and (iii) How to make sure that the application is indeed exclusively using pattern recognition for the expressed purpose (function creep (A. K. JAin et al., December 1998))? Solving these core problems will not only catapult Biometrics into mainstream applications but will also stimulate adoption of other pattern recognition applications for providing effective automation of sensitive tasks without jeopardizing our individual freedoms. For these reasons, we view Biometrics as a grand challenge - "a fundamental problem in science and engineering with broad economic and scientific impact".
Razib M. Othman - One of the best experts on this subject based on the ideXlab platform.
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multimodal Biometrics weighted score level fusion based on non ideal iris and face images
Expert Systems With Applications, 2014Co-Authors: Hishammuddin Asmuni, Rohayanti Hassan, Razib M. OthmanAbstract:The iris and face are among the most promising Biometric traits that can accurately identify a person because their unique textures can be swiftly extracted during the recognition process. However, unimodal Biometrics have limited usage since no single Biometric is sufficiently robust and accurate in real-world applications. Iris and face Biometric authentication often deals with non-ideal scenarios such as off-angles, reflections, expression changes, variations in posing, or blurred images. These limitations imposed by unimodal Biometrics can be overcome by incorporating multimodal Biometrics. Therefore, this paper presents a method that combines face and iris Biometric traits with the weighted score level fusion technique to flexibly fuse the matching scores from these two modalities based on their weight availability. The dataset use for the experiment is self established dataset named Universiti Teknologi Malaysia Iris and Face Multimodal Datasets (UTMIFM), UBIRIS version 2.0 (UBIRIS v.2) and ORL face databases. The proposed framework achieve high accuracy, and had a high decidability index which significantly separate the distance between intra and inter distance.
Zhe Jin - One of the best experts on this subject based on the ideXlab platform.
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A New Design for Alignment-Free Chaffed Cancelable Iris Key Binding Scheme
MDPI AG, 2019Co-Authors: Tong-yuen Chai, Bok-min Goi, Yong-haur Tay, Zhe JinAbstract:Iris has been found to be unique and consistent over time despite its random nature. Unprotected Biometric (iris) template raises concerns in security and privacy, as numerous large-scale iris recognition projects have been deployed worldwide—for instance, susceptibility to attacks, cumbersome renewability, and cross-matching. Template protection schemes from Biometric cryptosystems and cancelable Biometrics are expected to restore the confidence in Biometrics regarding data privacy, given the great advancement in recent years. However, a majority of the Biometric template protection schemes have uncertainties in guaranteeing criteria such as unlinkability, irreversibility, and revocability, while maintaining significant performance. Fuzzy commitment, a theoretically secure Biometric key binding scheme, is vulnerable due to the inherent dependency of the Biometric features and its reliance on error correction code (ECC). In this paper, an alignment-free and cancelable iris key binding scheme without ECC is proposed. The proposed system protects the binary Biometric data, i.e., IrisCodes, from security and privacy attacks through a strong and size varying non-invertible cancelable transform. The proposed scheme provides flexibility in system storage and authentication speed via controllable hashed code length. We also proposed a fast key regeneration without either re-enrollment or constant storage of seeds. The experimental results and security analysis show the validity of the proposed scheme
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ranking based locality sensitive hashing enabled cancelable Biometrics index of max hashing
IEEE Transactions on Information Forensics and Security, 2018Co-Authors: Zhe Jin, Jung Yeon Hwang, Soohyung Kim, Yenlung Lai, Andrew Beng Jin TeohAbstract:In this paper, we propose a ranking based locality sensitive hashing inspired two-factor cancelable Biometrics, dubbed “Index-of-Max” (IoM) hashing for Biometric template protection. With externally generated random parameters, IoM hashing transforms a real-valued Biometric feature vector into discrete index (max ranked) hashed code. We demonstrate two realizations from IoM hashing notion, namely Gaussian Random Projection based and Uniformly Random Permutation based hashing schemes. The discrete indices representation nature of IoM hashed codes enjoy several merits. Firstly, IoM hashing empowers strong concealment to the Biometric information. This contributes to the solid ground of non-invertibility guarantee. Secondly, IoM hashing is insensitive to the features magnitude, hence is more robust against Biometric features variation. Thirdly, the magnitude-independence trait of IoM hashing makes the hash codes being scale-invariant, which is critical for matching and feature alignment. The experimental results demonstrate favorable accuracy performance on benchmark FVC2002 and FVC2004 fingerprint databases. The analyses justify its resilience to the existing and newly introduced security and privacy attacks as well as satisfy the revocability and unlinkability criteria of cancelable Biometrics.
