scispace - formally typeset
Search or ask a question

Showing papers on "Biometrics published in 2004"


Journal ArticleDOI
TL;DR: A brief overview of the field of biometrics is given and some of its advantages, disadvantages, strengths, limitations, and related privacy concerns are summarized.
Abstract: A wide variety of systems requires reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user and no one else. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor. Biometric recognition, or, simply, biometrics, refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. By using biometrics, it is possible to confirm or establish an individual's identity based on "who she is", rather than by "what she possesses" (e.g., an ID card) or "what she remembers" (e.g., a password). We give a brief overview of the field of biometrics and summarize some of its advantages, disadvantages, strengths, limitations, and related privacy concerns.

4,678 citations


Journal ArticleDOI
TL;DR: Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests.
Abstract: Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests. The recognition principle is the failure of a test of statistical independence on iris phase structure encoded by multi-scale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 249 degrees of freedom and generates a discrimination entropy of about 3.2 b/mm/sup 2/ over the iris, enabling real-time decisions about personal identity with extremely high confidence. The high confidence levels are important because they allow very large databases to be searched exhaustively (one-to-many "identification mode") without making false matches, despite so many chances. Biometrics that lack this property can only survive one-to-one ("verification") or few comparisons. The paper explains the iris recognition algorithms and presents results of 9.1 million comparisons among eye images from trials in Britain, the USA, Japan, and Korea.

2,829 citations


Journal ArticleDOI
01 Aug 2004
TL;DR: This paper reviews recent developments and general strategies of the processing framework of visual surveillance in dynamic scenes, and analyzes possible research directions, e.g., occlusion handling, a combination of two and three-dimensional tracking, and fusion of information from multiple sensors, and remote surveillance.
Abstract: Visual surveillance in dynamic scenes, especially for humans and vehicles, is currently one of the most active research topics in computer vision. It has a wide spectrum of promising applications, including access control in special areas, human identification at a distance, crowd flux statistics and congestion analysis, detection of anomalous behaviors, and interactive surveillance using multiple cameras, etc. In general, the processing framework of visual surveillance in dynamic scenes includes the following stages: modeling of environments, detection of motion, classification of moving objects, tracking, understanding and description of behaviors, human identification, and fusion of data from multiple cameras. We review recent developments and general strategies of all these stages. Finally, we analyze possible research directions, e.g., occlusion handling, a combination of twoand three-dimensional tracking, a combination of motion analysis and biometrics, anomaly detection and behavior prediction, content-based retrieval of surveillance videos, behavior understanding and natural language description, fusion of information from multiple sensors, and remote surveillance.

2,321 citations


Book ChapterDOI
02 May 2004
TL;DR: This work provides formal definitions and efficient secure techniques for turning biometric information into keys usable for any cryptographic application, and reliably and securely authenticating biometric data.
Abstract: We provide formal definitions and efficient secure techniques for turning biometric information into keys usable for any cryptographic application, and reliably and securely authenticating biometric data.

1,914 citations


Journal ArticleDOI
TL;DR: The basic idea is that local sharp variation points, denoting the appearing or vanishing of an important image structure, are utilized to represent the characteristics of the iris.
Abstract: Unlike other biometrics such as fingerprints and face, the distinct aspect of iris comes from randomly distributed features. This leads to its high reliability for personal identification, and at the same time, the difficulty in effectively representing such details in an image. This paper describes an efficient algorithm for iris recognition by characterizing key local variations. The basic idea is that local sharp variation points, denoting the appearing or vanishing of an important image structure, are utilized to represent the characteristics of the iris. The whole procedure of feature extraction includes two steps: 1) a set of one-dimensional intensity signals is constructed to effectively characterize the most important information of the original two-dimensional image; 2) using a particular class of wavelets, a position sequence of local sharp variation points in such signals is recorded as features. We also present a fast matching scheme based on exclusive OR operation to compute the similarity between a pair of position sequences. Experimental results on 2 255 iris images show that the performance of the proposed method is encouraging and comparable to the best iris recognition algorithm found in the current literature.

999 citations


Journal ArticleDOI
18 May 2004
TL;DR: This work presents various methods that monolithically bind a cryptographic key with the biometric template of a user stored in the database in such a way that the key cannot be revealed without a successful biometric authentication.
Abstract: In traditional cryptosystems, user authentication is based on possession of secret keys; the method falls apart if the keys are not kept secret (i.e., shared with non-legitimate users). Further, keys can be forgotten, lost, or stolen and, thus, cannot provide non-repudiation. Current authentication systems based on physiological and behavioral characteristics of persons (known as biometrics), such as fingerprints, inherently provide solutions to many of these problems and may replace the authentication component of traditional cryptosystems. We present various methods that monolithically bind a cryptographic key with the biometric template of a user stored in the database in such a way that the key cannot be revealed without a successful biometric authentication. We assess the performance of one of these biometric key binding/generation algorithms using the fingerprint biometric. We illustrate the challenges involved in biometric key generation primarily due to drastic acquisition variations in the representation of a biometric identifier and the imperfect nature of biometric feature extraction and matching algorithms. We elaborate on the suitability of these algorithms for digital rights management systems.

942 citations


Journal ArticleDOI
TL;DR: This paper proposed a novel two factor authenticator based on iterated inner products between tokenised pseudo-random number and the user specific fingerprint feature, which generated from the integrated wavelet and Fourier–Mellin transform, and hence produce a set of user specific compact code that coined as BioHashing.

765 citations


Proceedings ArticleDOI
06 Sep 2004
TL;DR: The various scenarios that are possible in multimodal biometric systems, the levels of fusion that are plausible and the integration strategies that can be adopted to consolidate information are discussed.
Abstract: Unimodal biometric systems have to contend with a variety of problems such as noisy data, intra-class variations, restricted degrees of freedom, non-universality, spoof attacks, and unacceptable error rates. Some of these limitations can be addressed by deploying multimodal biometric systems that integrate the evidence presented by multiple sources of information. This paper discusses the various scenarios that are possible in multimodal biometric systems, the levels of fusion that are plausible and the integration strategies that can be adopted to consolidate information. We also present several examples of multimodal systems that have been described in the literature.

695 citations


Proceedings ArticleDOI
23 Aug 2004
TL;DR: A new method for extracting features from palmprints using the competitive coding scheme and angular matching and the execution time for the whole process of verification, including preprocessing, feature extraction and final matching is 1s.
Abstract: There is increasing interest in the development of reliable, rapid and non-intrusive security control systems. Among the many approaches, biometrics such as palmprints provide highly effective automatic mechanisms for use in personal identification. This paper presents a new method for extracting features from palmprints using the competitive coding scheme and angular matching. The competitive coding scheme uses multiple 2-D Gabor filters to extract orientation information from palm lines. This information is then stored in a feature vector called the competitive code. The angular matching with an effective implementation is then defined for comparing the proposed codes, which can make over 9,000 comparisons within 1s. In our testing database of 7,752 palmprint samples from 386 palms, we can achieve a high genuine acceptance rate of 98.4% and a low false acceptance rate of 3/spl times/10/sup -6/%. The execution time for the whole process of verification, including preprocessing, feature extraction and final matching, is 1s.

562 citations


Journal ArticleDOI
TL;DR: The latest research indicates using a combination of biometric avenues for human identification is more effective, and far more challenging, than using just one method.
Abstract: The latest research indicates using a combination of biometric avenues for human identification is more effective, and far more challenging.

494 citations


Journal ArticleDOI
TL;DR: The main objective of this paper is to review the extensive research that has been done on fingerprint classification over the last four decades and discusses the fingerprint features that are useful for distinguishing fingerprint classes and reviews the methods of classification that have been applied to the problem.
Abstract: Biometrics is the automatic identification of an individual that is based on physiological or behavioural characteristics. Due to its security-related applications and the current world political climate, biometrics is currently the subject of intense research by both private and academic institutions. Fingerprints are emerging as the most common and trusted biometric for personal identification. The main objective of this paper is to review the extensive research that has been done on fingerprint classification over the last four decades. In particular, it discusses the fingerprint features that are useful for distinguishing fingerprint classes and reviews the methods of classification that have been applied to the problem. Finally, it presents empirical results from the state of the art fingerprint classification systems that have been tested using the NIST Special Database 4.

Book ChapterDOI
TL;DR: Experiments show that the recognition performance of a fingerprint system can be improved significantly by using additional user information like gender, ethnicity, and height and a framework for integrating the ancillary information with the output of a primary biometric system is presented.
Abstract: Many existing biometric systems collect ancillary information like gender, age, height, and eye color from the users during enrollment. However, only the primary biometric identifier (fingerprint, face, hand-geometry, etc.) is used for recognition and the ancillary information is rarely utilized. We propose the utilization of “soft” biometric traits like gender, height, weight, age, and ethnicity to complement the identity information provided by the primary biometric identifiers. Although soft biometric characteristics lack the distinctiveness and permanence to identify an individual uniquely and reliably, they provide some evidence about the user identity that could be beneficial. This paper presents a framework for integrating the ancillary information with the output of a primary biometric system. Experiments conducted on a database of 263 users show that the recognition performance of a fingerprint system can be improved significantly (≈ 5%) by using additional user information like gender, ethnicity, and height.

Proceedings Article
18 Jun 2004
TL;DR: A brief overview of biometric methods, both unimodal and multimodal, and their advantages and disadvantages, will be presented.
Abstract: Biometric recognition refers to an automatic recognition of individuals based on a feature vector (s) derived from their physiological and/or behavioral characteristic. Biometric recognition systems should provide a reliable personal recognition schemes to either confirm or determine the identity of an individual. Applications of such a system include computer systems security, secure electronic banking, mobile phones, credit cards, secure access to buildings, health and social services. By using biometrics a person could be identified based on "who she/he is" rather then "what she/he has" (card, token, key) or "what she/he knows" (password, PIN). In this paper, a brief overview of biometric methods, both unimodal and multimodal, and their advantages and disadvantages, will be presented.

Proceedings ArticleDOI
25 Aug 2004
TL;DR: In this article, an effective live face detection algorithm is presented based on the analysis of Fourier spectra of a single face image or face image sequences using structure and movement information of live face.
Abstract: Biometrics is a rapidly developing technology that is to identify a person based on his or her physiological or behavioral characteristics. To ensure the correction of authentication, the biometric system must be able to detect and reject the use of a copy of a biometric instead of the live biometric. This function is usually termed "liveness detection". This paper describes a new method for live face detection. Using structure and movement information of live face, an effective live face detection algorithm is presented. Compared to existing approaches, which concentrate on the measurement of 3D depth information, this method is based on the analysis of Fourier spectra of a single face image or face image sequences. Experimental results show that the proposed method has an encouraging performance.

Journal ArticleDOI
TL;DR: A human recognition algorithm by combining static and dynamic body biometrics, fused on the decision level using different combinations of rules to improve the performance of both identification and verification is described.
Abstract: Vision-based human identification at a distance has recently gained growing interest from computer vision researchers. This paper describes a human recognition algorithm by combining static and dynamic body biometrics. For each sequence involving a walker, temporal pose changes of the segmented moving silhouettes are represented as an associated sequence of complex vector configurations and are then analyzed using the Procrustes shape analysis method to obtain a compact appearance representation, called static information of body. In addition, a model-based approach is presented under a Condensation framework to track the walker and to further recover joint-angle trajectories of lower limbs, called dynamic information of gait. Both static and dynamic cues obtained from walking video may be independently used for recognition using the nearest exemplar classifier. They are fused on the decision level using different combinations of rules to improve the performance of both identification and verification. Experimental results of a dataset including 20 subjects demonstrate the feasibility of the proposed algorithm.

Proceedings ArticleDOI
22 Jun 2004
TL;DR: This paper proposes an attack system that uses a hill climbing procedure to synthesize the target minutia templates and evaluates its feasibility with extensive experimental results conducted on a large fingerprint database.
Abstract: In spite of numerous advantages of biometrics-based personal authentication systems over traditional security systems based on token or knowledge, they are vulnerable to attacks that can decrease their security considerably. In this paper, we analyze these attacks in the realm of a fingerprint biometric system. We propose an attack system that uses a hill climbing procedure to synthesize the target minutia templates and evaluate its feasibility with extensive experimental results conducted on a large fingerprint database. Several measures that can be utilized to decrease the probability of such attacks and their ramifications are also presented.

Proceedings ArticleDOI
20 Sep 2004
TL;DR: This paper proposes a novel scheme that encrypts the training images used to synthesize the single minimum average correlation energy filter for biometric authentication, and shows analytically that the recognition performance remains invariant to the proposed encryption scheme, while retaining the desired shift-invariance property of correlation filters.
Abstract: In this paper, we address the issue of producing cancelable biometric templates; a necessary feature in the deployment of any biometric authentication system. We propose a novel scheme that encrypts the training images used to synthesize the single minimum average correlation energy filter for biometric authentication. We show theoretically that convolving the training images with any random convolution kernel prior to building the biometric filter does not change the resulting correlation output peak-to-sidelobe ratios, thus preserving the authentication performance. However, different templates can be obtained from the same biometric by varying the convolution kernels thus enabling the cancelability of the templates. We evaluate the proposed method using the illumination subset of the CMU pose, illumination, and expressions (PIE) face dataset. Our proposed method is very interesting from a pattern recognition theory point of view, as we are able to 'encrypt' the data and perform recognition in the encrypted domain that performs as well as the unencrypted case, regardless of the encryption kernel used; we show analytically that the recognition performance remains invariant to the proposed encryption scheme, while retaining the desired shift-invariance property of correlation filters.

Proceedings ArticleDOI
23 Aug 2004
TL;DR: 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 the authors' individual freedoms.
Abstract: 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".

Journal ArticleDOI
01 Sep 2004
TL;DR: As the deficiencies of traditional password-based access systems become increasingly acute, researchers have turned their focus to keystroke biometrics, which seeks to identify individuals by their typing characteristics.
Abstract: As the deficiencies of traditional password-based access systems become increasingly acute, researchers have turned their focus to keystroke biometrics, which seeks to identify individuals by their typing characteristics. However, this field still faces many challenges before it can see full acceptance.

Journal ArticleDOI
TL;DR: An attempt to reflect shape information of the iris by analyzing local intensity variations of an iris image by constructing a set of one-dimensional intensity signals that reflect to a large extent their various spatial modes and are used as distinguishing features.

Book ChapterDOI
01 Jan 2004
TL;DR: A large human gait database is designed and built, providing a large multi-purpose dataset enabling the investigation of gait as a biometric and is also a useful database for many still and sequence based vision applications.
Abstract: Biometrics today include recognition by characteristic and by behaviour. Of these, face recognition is the most established with databases having evolved from small single shot single view databases, through multi-shot multi-view and on to current video-sequence databases. Results and potential of a new biometric are revealed primarily by the database on which new techniques are evaluated. Clearly, to ascertain the potential of gait as a biometric, a sequence-based database consisting of many subjects with multiple samples is needed. A large database enables the study of inter-subject variation. Further, issues concerning scene noise (or non-ideal conditions) need to be studied, ideally with a link between ground truth and application based analysis. Thus, we have designed and built a large human gait database, providing a large multi-purpose dataset enabling the investigation of gait as a biometric. In addition, it is also a useful database for many still and sequence based vision applications.

Journal ArticleDOI
TL;DR: Matching results on a database of 50 different fingers, with 200 impressions per finger, indicate that a systematic template selection procedure as presented here results in better performance than random template selection.

Journal ArticleDOI
TL;DR: This paper points out that ElGamal's fingerprint-based remote user authentication scheme is vulnerable to masquerade attack, and proposes a new scheme to enhance their security.

Proceedings ArticleDOI
25 Aug 2004
TL;DR: The motivation for utilizing soft biometric information is presented and how thesoft biometric traits can be automatically extracted and incorporated in the decision making process of the primary biometric system is analyzed.
Abstract: Biometrics is rapidly gaining acceptance as the technology that can meet the ever increasing need for security in critical applications. Biometric systems automatically recognize individuals based on their physiological and behavioral characteristics. Hence, the fundamental requirement of any biometric recognition system is a human trait having several desirable properties like universality, distinctiveness, permanence, collectability, acceptability, and resistance to circumvention. However, a human characteristic that possesses all these properties has not yet been identified. As a result, none of the existing biometric systems provide perfect recognition and there is a scope for improving the performance of these systems. Although characteristics like gender, ethnicity, age, height, weight and eye color are not unique and reliable, they provide some information about the user. We refer to these characteristics as "soft" biometric traits and argue that these traits can complement the identity information provided by the primary biometric identifiers like fingerprint and face. This paper presents the motivation for utilizing soft biometric information and analyzes how the soft biometric traits can be automatically extracted and incorporated in the decision making process of the primary biometric system. Preliminary experiments were conducted on a fingerprint database of 160 users by synthetically generating soft biometric traits like gender, ethnicity, and height based on known statistics. The results show that the use of additional soft biometric user information significantly improves (approximately 6%) the recognition performance of the fingerprint biometric system.

Journal Article
TL;DR: The proposed algorithm is highly robust against fingerprint image degradation due to inadequate fingertip conditions and exhibits efficient identification performance even for difficult fingerprint images that could not have been captured by a pressure sensitive fingerprint sensor.
Abstract: This paper presents an algorithm for fingerprint matching using the Phase-Only Correlation (POC) function. One of the most difficult problems in human identification by fingerprints has been that the matching performance is significantly influenced by fingertip surface condition, which may vary depending on environmental or personal causes. This paper proposes a new fingerprint matching algorithm using phase spectra of fingerprint images. The proposed algorithm is highly robust against fingerprint image degradation due to inadequate fingertip conditions. A set of experiments is carried out using fingerprint images captured by a pressure sensitive fingerprint sensor. The proposed algorithm exhibits efficient identification performance even for difficult fingerprint images that could not be identified by the conventional matching algorithms. key words: phase-only correlation, phase-only matched filtering, phase correlation, biometrics, fingerprint verification, fingerprint identification

Posted Content
Pim Tuyls1, Jasper Goseling1
TL;DR: In this paper, the secrecy capacity of biometric authentication systems is investigated for the discrete and continuous case, and a general algorithm that meets the requirements and achieves Cs as well as Cid (the identification capacity).
Abstract: In this paper, we formulate the requirements for privacy protecting biometric authentication systems. The secrecy capacity Cs is investigated for the discrete and the continuous case. We present, furthermore, a general algorithm that meets the requirements and achieves Cs as well as Cid (the identification capacity). Finally, we present some practical constructions of the general algorithm and analyze their properties.

Proceedings ArticleDOI
25 Aug 2004
TL;DR: In this paper, the authors describe a new behavioural biometric technique based on human computer interaction, which captures the user interaction via a pointing device, and uses this behavioral information to verify the identity of an individual.
Abstract: In this paper we describe a new behavioural biometric technique based on human computer interaction. We developed a system that captures the user interaction via a pointing device, and uses this behavioural information to verify the identity of an individual. Using statistical pattern recognition techniques, we developed a sequential classifier that processes user interaction, according to which the user identity is considered genuine if a predefined accuracy level is achieved, and the user is classified as an impostor otherwise. Two statistical models for the features were tested, namely Parzen density estimation and a unimodal distribution. The system was tested with different numbers of users in order to evaluate the scalability of the proposal. Experimental results show that the normal user interaction with the computer via a pointing device entails behavioural information with discriminating power, that can be explored for identity authentication.

Book ChapterDOI
TL;DR: This paper discusses the problem of biometric sensor interoperability in biometric systems and presents a case study involving two different fingerprint sensors.
Abstract: The problem of biometric sensor interoperability has received limited attention in the literature. Most biometric systems operate under the assumption that the data (viz., images) to be compared are obtained using the same sensor and, hence, are restricted in their ability to match or compare biometric data originating from different sensors. Although progress has been made in the development of common data exchange formats to facilitate the exchange of feature sets between vendors, very little effort has been invested in the actual development of algorithms and techniques to match these feature sets. In the Fingerprint Verification Competition (FVC 2002), for example, the evaluation protocol only matched images originating from the same sensor although fingerprint data from 3 different commercial sensors was available. This is an indication of the difficulty in accommodating sensor interoperability in biometric systems. In this paper we discuss this problem and present a case study involving two different fingerprint sensors.

Journal ArticleDOI
TL;DR: The state-of-the-art of several popular biometric modalities and technologies are outlined and specific applications where biometric recognition may be beneficially incorporated and integration strategies of biometric authentication technologies into DRM systems that satisfy the needs and requirements of consumers, content providers, and payment brokers are discussed.
Abstract: Securing the exchange of intellectual property and providing protection to multimedia contents in distribution systems have enabled the advent of digital rights management (DRM) systems. User authentication, a key component of any DRM system, ensures that only those with specific rights are able to access the digital information. It is here that biometrics play an essential role. It reinforces security at all stages where customer authentication is needed. Biometric recognition, as a means of personal authentication, is an emerging signal processing area focused on increasing security and convenience of use in applications where users need to be securely identified. In this article, we outline the state-of-the-art of several popular biometric modalities and technologies and provide specific applications where biometric recognition may be beneficially incorporated. In addition, the article also discussed integration strategies of biometric authentication technologies into DRM systems that satisfy the needs and requirements of consumers, content providers, and payment brokers, securing delivery channels and contents.

Journal ArticleDOI
TL;DR: It is argued that the similarity plot encodes a projection of gait dynamics, which is also correspondence-free, robust to segmentation noise, and works well with low-resolution video.
Abstract: Gait is one of the few biometrics that can be measured at a distance, and is hence useful for passive surveillance as well as biometric applications. Gait recognition research is still at its infancy, however, and we have yet to solve the fundamental issue of finding gait features which at once have suffcient discrimination power and can be extracted robustly and accurately from low-resolution video. This paper describes a novel gait recognition technique based on the image self-similarity of a walking person. We contend that the similarity plot encodes a projection of gait dynamics. It is also correspondence-free, robust to segmentation noise, and works well with low-resolution video. The method is tested on multiple data sets of varying sizes and degrees of diffculty. Performance is best for fronto-parallel viewpoints, whereby a recognition rate of 98% is achieved for a data set of 6 people, and 70% for a data set of 54 people.