Automatic biometric recognition techniques are becoming increasingly important in corporate and public security systems. The term "biometric" is derived from the Greek words bio (life) and metric (to measure). There are two types of biometrics that can be used for human identification or verification: physical biometrics and behavioral biometrics. Physical biometrics, such as fingerprint and iris, have already been widely acknowledged and used in many real applications. As a relatively new technology, behavioral biometrics help verify a person's identity through some measurable activity patterns, e.g., speaker recognition (i.e., analyzing vocal behavior), signature recognition (i.e., analyzing signature dynamics), gait recognition (i.e., analyzing walking patterns), keystroke dynamics (i.e., analyzing keyboard typing patterns), mouse dynamics (i.e., analyzing mouse moving patterns), etc.
Biometrics has been studied for many years. However, up to the present, most work is on physical biometrics, which naturally becomes main contents of the existing publications. Although some of them mentioned behavioral biometrics as an important category in addition to physical biometrics, they did not provide comprehensive survey and profound insight into this emerging technique. Over the past few years, people with both academic and industrial background have begun to realize the importance and advantages of behavioral biometrics, which consequently leads to a rapid development in this exciting area. Therefore it is now very opportune to summarize what has happened and to indicate what will happen in this exciting area. To the best of our knowledge, this edited book is the first literature mainly focusing on behavioral biometrics, which will be very attractive and useful to those who are interested in these innovative recognition techniques and their promising applications. In particular, this book covers as much as possible about behavioral biometrics including basic knowledge, the state-of-the-art techniques, and realistic application systems, which will be very helpful to both researchers and practitioners.
The objective of this book is to discuss typical behavioral biometrics and to collect the latest advances in behavioral biometric techniques including both theoretical approaches and real applications. This edited book is anticipated to provide researchers and practitioners a comprehensive understanding of the start-of-the-art of behavioral biometrics techniques, potential applications, successful practice, available resources, etc. The book can serve as an important reference tool for researchers and practitioners in biometrics recognition, a handbook for research students and a repository for technologists.
The target audience of this book includes the professionals and researchers working in the field of various disciplines, e.g. computer vision, pattern recognition, information technique, psychology, image processing, artificial intelligence, etc. In particular, this book provides a comprehensive introduction to the latest techniques in behavioral biometrics for researchers. The book also serves as an important reference tool for both researchers and practitioners working on biometric recognition, a handbook for research students and a repository for technologists. It is also attractive to the managers of those organizations seeking reliable security solutions.
In this edition, many topics of interest are highlighted. The following we give some brief introductions to each chapter included in this book.
Chapter 1, “Taxonomy of Behavioural Biometrics”, presents taxonomy of the state-of-the-art in behavioural biometrics which are based on skills, style, preference, knowledge, motor-skills or strategy used by people while accomplishing different everyday tasks such as driving an automobile, talking on the phone or using a computer. Current research in the field is examined and analyzed along with the features used to describe different types of behaviours. After comparing accuracy rates for verification of users using different behavioural biometric approaches researchers address privacy issues which arise or might arise in the future with the use of behavioural biometrics. Finally, generalized properties of behaviour are addressed as well as influence of environmental factors on observed behaviour and potential directions for future research in behavioural biometrics.
Chapter 2, “Security Evaluation of Behavioral Biometric Systems”, reviews the state of the art of the methodology for evaluating the security of biometric systems, in particular of behavioral biometric verification systems. For increasing the confidence in the security of IT products, security evaluations by independent third-party testing laboratories are the first choice. In some fields of application of biometric methods (e.g. for protecting private keys for qualified electronic signatures), a security evaluation is even required by legislation. The common criteria for IT security evaluation form the basis for security evaluations for which a wide international recognition is desired. Within the common criteria, predefined security assurance require¬ments describe actions to be carried out by the developers of the product and by the evaluators. The assurance components that require clarifica¬tion in the context of biometric systems are related to vulnerability assessment.
Chapter 3, “Performance Evaluation of Behavioral Biometric Systems”, presents an overview of techniques for the performance evaluation of behavioral biometric systems. The BioAPI standard that defines the architecture of a biometric system is presented. The general methodology for the evaluation of biometric systems is given including statistical metrics, definition of benchmark databases and subjective evaluation. These considerations rely with the ISO/IEC19795-1 standard describing the biometric performance testing and reporting. The specificity of behavioral biometric systems is detailed in order to define some additional constraints for their evaluation. This chapter is dedicated to researchers and engineers who need to quantify the performance of such biometric systems.
Chapter 4, “Individual Identification from Video Based on ‘Behavioural Biometrics’”, presents multiple methods for recognizing individuals from their “style of action/actions”, i.e. “biometric behavioural characteristics”. Two forms of human recognition can be useful: the determination that an object is from the class of humans (i.e., human detection), and the determination that an object is a particular individual from this class (i.e., individual recognition). For individual recognition, this chapter considers two different categories, firstly individual recognition using “style of single action” (i.e. hand waving and partial gait) and secondly individual recognition using “style of doing similar actions” in video sequences. The “style of single action” and “style of doing similar actions”, are proposed as a cue to discriminate between two individuals. Nowadays multi-biometric security systems are available to recognise individuals from video sequences. This chapter also reports multiple novel behavioural biometric techniques for individual recognition based on “style of single action” and “style of multiple actions”, which can be additionally combined with finger print, face, voice and iris biometrics as a complementary cue to intelligent security systems.
Chapter 5, “Behavioral Biometrics: A Biosignal Based Approach”, discusses the use of biosignal based biometrics, highlighting key studies and how this approach can be integrated into a multi-biometric user authentication system. The deployment of behavioral biometrics relies on the way a person interacts with an authentication device. Typical instances of this approach include voice, signature, and keystroke dynamics. Novel approaches to behavioral biometrics include biosignals such as the electroencephalogram and the electrocardiogram. The biosignal approach to user authentication has been shown to produce equal error rates on par with more traditional behavioral biometric approaches. Through a process similar to biofeedback, users can be trained with minimal effort to produce computer-based input via the manipulations of endogenous biosignal patterns.
Chapter 6, “Gabor Wavelets in Behavioral Biometrics”, provides a brief discussion on the origin of Gabor wavelets, and then an illustration of “how to use Gabor wavelets” to extract features for a generic biometric application is discussed. Gabor wavelets are employed regularly in various Biometrics applications because of their biological relevance and computational properties. These wavelets exhibit desirable characteristics of spatial locality and orientation selectivity, and are optimally localized in the space and frequency domains. Physiological, biometric systems such as face, fingerprint, and iris based human identification have shown great improvement in identification accuracy if Gabor wavelets are used for feature extraction. Moreover, some behavioral biometric systems such as speaker and gait-based applications have shown a more than 7% increase in identification accuracy. This study also provides an implementation pseudocode for the wavelet, as well as presenting an elaborate discussion on biometric applications with specific emphasis on behavioral biometric systems that have used Gabor wavelets and providing guideline for some biometric systems that have not yet applied Gabor for feature extraction.
Chapter 7, “Gait Recognition and Analysis”, provides a survey of recent advances in gait recognition. With the increasing demands of visual surveillance systems, human identification at a distance is an urgent need. Gait is an attractive biometric feature for human identification at a distance, and recently has gained much interest from computer vision researchers. Firstly an overview on gait recognition framework, feature extraction and classifiers is given in this chapter, and then some gait databases and evaluation metrics are introduced. Finally, research challenges and applications are discussed in detail.
Chapter 8, “Multilinear Modeling for Robust Identity Recognition from Gait”, illustrates a three-layer scheme in which image sequences are first mapped to observation vectors of fixed dimension using Markov modeling, to be later classified by an asymmetric bilinear model, for human identification from gait. Gait recognition is a challenging task in realistic surveillance scenarios in which people walking along arbitrary directions are shot by a single camera. However, viewpoint is only one of the many covariate factors limiting the efficacy of gait recognition as a reliable biometrics. In this chapter the problem of robust identity recognition in the framework of multilineal models is addressed. Bilinear models, in particular, allow one to classify the “content” of human motions of unknown “style” (covariate factor). Tests are shown on the CMU Mobo database to prove that bilinear separation outperforms other common approaches, allowing robust view- and action-invariant identity recognition. In addition, an overview of the available tensor factorization techniques is given, and their potential applications to gait recognition are outlined.
Chapter 9, “Gait Feature Fusion using Factorial HMM”, explores the factorial hidden Markov model (FHMM), an extended hidden Markov model (HMM) with a multiple layer structure, as a feature fusion framework for gait recognition. FHMM provides an alternative to combining several gait features without concatenating them into a single augmented feature, thus, to some extent, overcomes the curse of dimensionality and small sample size problem for gait recognition. Three gait features, the frieze feature, wavelet feature, and boundary signature, are adopted in the numerical experiments conducted on CMU MoBo database and CASIA gait database A. Besides the cumulative matching score (CMS) curves, McNemar’s test is employed to check on the statistical significance of the performance difference between the recognition algorithms. Experimental results demonstrate that the proposed FHMM feature fusion scheme outperforms the feature concatenation method.
Chapter 10, “Mouse Dynamics Biometric Technology”, introduces the concepts behind the mouse dynamics biometric technology. Mouse dynamics can be described as the characteristics of the actions received from the mouse input device for a user, while interacting with a graphical user interface. One of its key strengths compared to traditional biometric technologies is that it allows dynamic and passive user monitoring. As such it can be used to track reliably and continuously legitimate and illegitimate users throughout computing sessions. This chapter presents a generic architecture of the detector used to collect and process mouse dynamics, and studies the various factors used to build the user’s signature. This chapter also provides an updated survey on the researches and industrial implementations related to the technology, and studies possible applications in computer security.
Chapter 11, “Activity and Individual Human Recognition in Infrared Imagery”, investigates repetitive human activity patterns and individual recognition in thermal infrared imagery, where human motion can be easily detected from the background regardless of the lighting conditions and colors of the human clothing and surfaces, and backgrounds. An efficient spatio-temporal representation for human repetitive activity and individual recognition, which represents human motion sequence in a single image while preserving spatio-temporal characteristics, is employed. A statistical approach is used to extract features for activity and individual recognition. Experimental results show that the approach achieves good performance for repetitive human activity and individual recognition.
Chapter 12, “Gaze Based Personal Identification”, describes the use of visual attention characteristics as a biometric for authentication or identification of individual viewers. The visual attention characteristics of a person can be easily monitored by tracking the gaze of a viewer during the presentation of a known or unknown visual scene. The positions and sequences of gaze locations during viewing may be determined by overt (conscious) or covert (sub-conscious) viewing behaviour. Methods to quantify the spatial and temporal patterns established by the viewer for both overt and covert behaviours are proposed. The former behaviour entails a simple PIN-like approach to develop an independent signature while the latter behaviour is captured through three proposed techniques: a principal component analysis technique (‘eigenGaze’); a linear discriminant analysis technique; and a fusion of distance measures. Experimental results suggest that both types of gaze behaviours can provide simple and effective biometrics for this application.
Chapter 13, “Speaker Verification and Identification”, introduces several speaker recognition systems and examines their performances under various conditions. Speaker recognition can be classified into either speaker verification or speaker identification. Both the speaker verification and identification system consist of three essential elements: feature extraction, speaker modeling, and matching. The feature extraction pertains to extracting essential features from an input speech for speaker recognition. The speaker modeling pertains to probabilistically modeling the feature of the enrolled speakers. The matching pertains to matching the input feature to various speaker models. Speaker modeling techniques including Gaussian mixture model (GMM), hidden Markov model (HMM), and phone n-grams are presented, and their performances are compared under various tasks. Several verification and identification experimental results presented in this chapter indicates that speaker recognition performances are highly dependent on the acoustical environment. A comparative study between human listeners and an automatic speaker verification system is presented, and it indicates that an automatic speaker verification system can outperform human listeners. The applications of speaker recognition are summarized, and finally various obstacles that must be overcome are discussed.
Chapter 14, “Visual Attention for Behavioral Biometric Systems”, proposes novel behavioral biometric applications based on the human visual attention system. More in detail, two biometrics systems based on how humans recognize faces, bodies, postures, etc, are discussed, according to the distribution of the focuses of attention (FOAs, that represent the most interesting parts in a visual scene) that are fixations reproducing the ability of humans in the interpretation of visual scenes. Indeed the pattern of these fixations and the choice of where to send the eye next are not random but appear to be guided.
Chapter 15, “Statistical Features for Text-independent Writer Identification”, presents three statistical feature models of handwritings in paragraph-level, stroke-level and point-level respectively for text-independent writer identification. Automatic writer identification is desirable in many important applications including banks, forensics, archeology, etc. A key and still open issue in writer identification is how to represent the distinctive and robust features of individual handwriting. The proposed three methods evolves from coarse to fine, showing the technology roadmap of handwriting biometrics, and are evaluated on CASIA handwriting databases. Experimental results show that they perform well in both Chinese and English handwriting datasets, and fine scale handwriting primitives are advantageous in text-independent writer identification. The best performing method adopts the probability distribution function and the statistical dynamic features of tri-point primitives for handwriting feature representation, achieving 95% writer identification accuracy on CASIA-HandwritingV2 with 1,500 handwritings from more than 250 subjects. And a demo system of online writer identification is developed to demonstrate the potential of current algorithms for real-world applications.
Chapter 16, “Keystroke Biometric Identification and Authentication on Long-Text Input”, introduces a novel keystroke biometric system for long-text input for identification and authentication applications. The system consists of a Java applet to collect raw keystroke data over the internet, a feature extractor, and pattern classifiers to make identification or authentication decisions. Experiments on over 100 subjects investigated two input modes – copy and free-text input – and two keyboard types – desktop and laptop keyboards. The system can accurately identify or authenticate individuals if the same type of keyboard is used to produce the enrollment and questioned input samples. Longitudinal experiments quantified performance degradation over intervals of several weeks and over an interval of two years. Additional experiments investigated the system’s hierarchical model, parameter settings, assumptions, and sufficiency of enrollment samples and input-text length.
Chapter 17, “Secure Dynamic Signature-Crypto Key Computation”, reports a dynamic hand signatures-key generation scheme which is based on a randomized biometric helper. Biometric-key computation is a process of converting a piece of live biometric data into a key. Among the various biometrics available today, the hand signature has the highest level of social acceptance. On the other hand, cryptography is used in multitude applications present in technologically advanced society. The signature crypto-key computation is hence of highly interesting as it is a way to integrate behavioral biometrics with the existing cryptographic framework. This proposed scheme consists of a randomized feature discretization process and a code redundancy construction. The former enables one to control the intraclass variations of dynamic hand signatures to the minimal level and the latter will further reduce the errors. Randomized biometric helper ensures that a signature-key is easy to be revoked when the key is compromised. The proposed scheme is evaluated based on the 2004 Signature Verification Competition (SVC) database, and results show that the proposed methods are able to produce keys that are stable, distinguishable and secure.
Chapter 18, “Game Playing Tactic as a Behavioral Biometric for Human Identification”, expends behavior based intrusion detection approach to a new domain of game networks. Specifically, this research shows that a behavioral biometric signature can be generated based on the strategy used by an individual to play a game. Software capable of automatically extracting behavioral profiles for each player in a game of Poker is written. Once a behavioral signature is generated for a player, it is continuously compared against player’s current actions. Any significant deviations in behavior are reported to the game server administrator as potential security breaches. In this chapter experimental results with user verification and identification as well as our approach to generation of synthetic poker data and potential spoofing approaches of the developed system are reported. Also, utilizing techniques developed for behavior based recognition of humans to the identification and verification of intelligent game bots is proposed.
Chapter 19, “Multi-Modal Biometrics Fusion for Human Recognition in Video”, introduces a new video based recognition system to recognize non-cooperating individuals at a distance in video, who expose side views to the camera. Information from two biometric sources, side face and gait, is utilized and integrated for recognition. For side face, an Enhanced Side Face Image (ESFI), a higher resolution image compared with the image directly obtained from a single video frame, is constructed, which integrates face information from multiple video frames. For gait, the Gait Energy Image (GEI), a spatio-temporal compact representation of gait in video, is used to characterize human walking properties. The features of face and gait are extracted from ESFI and GEI, respectively. They are integrated at both of the match score level and the feature level by using different fusion strategies. The system is tested on a database of video sequences, corresponding to 45 people, which are collected over several months. The performance of different fusion methods are compared and analyzed. The experimental results show that (a) the idea of constructing ESFI from multiple frames is promising for human recognition in video and better face features are extracted from ESFI compared to those from the original side face images; (b) the synchronization of face and gait is not necessary for face template ESFI and gait template GEI; (c) integrated information from side face and gait is effective for human recognition in video. The feature level fusion methods achieve better performance than the match score level methods fusion overall.
This book is an immediate and timely effort to review the latest progress in behavioral biometrics. Attempts of behavioral biometrics recognition in real-world applications bring more realistic challenging problems in addition to the theoretical methodologies. As a reference book on various behavioral biometrics technologies, it contains an excellent collection of technical chapters written by authors who are worldwide recognized researchers and practitioners on the corresponding topics.
The readers of this book can learn about the state of the art of behavioral biometric techniques; They can be inspired of research interest in this exciting and promising area; They can be guided how to follow the right ways for their specific research and applications; They can learn about the potential applications of behavioral biometrics systems, research challenges and possible solutions.
Dr. Liang Wang,
The University of Melbourne, Australia
Dr. Xin Geng,
Southeast University, China