Multimodal Biometrics Fusion for Human Recognition in Video

This chapter introduces a new video based recognition system to recognize noncooperating 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 spatiotemporal 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.

Secure Dynamic Signature-Crypto Key Computation

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. The general masses are familiar with the use of handwritten signature by means of verification and acknowledgement. On the other hand, cryptography is used in multitude applications present in technologically advanced society. Examples include the security of ATM cards, computer networks, and e-commerce. The signature crypto-key computation is hence of highly interesting as it is a way to integrate behavioral biometrics with the existing cryptographic framework. In this chapter, we report a dynamic hand signatures-key generation scheme which is based on a randomized biometric helper. This 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. We found that the proposed methods are able to produce keys that are stable, distinguishable, and secure.

Keystroke Biometric Identification and Authentication on Long-Text Input

A novel keystroke biometric system for long-text input was developed and evaluated for user 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. Although evaluated on input texts up to 650 keystrokes, we found that input of 300 keystrokes, roughly four lines of text, is sufficient for the important applications described.

Game Playing Tactic as a Behavioral Biometric for Human Identification

This chapter expends behavior based intrusion detection approach to a new domain of game networks. Specifically, our research shows that a behavioral biometric signature can be generated based on the strategy used by an individual to play a game. We wrote software capable of automatically extracting behavioral profiles for each player in a game of poker. 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, we report our 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. We also propose utilizing techniques developed for behavior based recognition of humans to the identification and verification of intelligent game bots. Our experimental results demonstrate feasibility of such methodology.

Gaze Based Personal Identification

This chapter 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 (subconscious) 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.

Speaker Verification and Identification

A speaker recognition system verifies or identifies a speaker’s identity based on his/her voice. It is considered as one of the most convenient biometric characteristic for human machine communication. This chapter introduces several speaker recognition systems and examines their performances under various conditions. Speaker recognition can be classified into either speaker verification or speaker identification. Speaker verification aims to verify whether an input speech corresponds to a claimed identity, and speaker identification aims to identify an input speech by selecting one model from a set of enrolled speaker models. 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 in this chapter, their performances are compared under various tasks. Several verification and identification experimental results presented in this chapter indicate 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.

Activity and Individual Human Recognition in Infrared Imagery

In this chapter the Authors introduce the concepts behind the mouse dynamics biometric technology, present a generic architecture of the detector used to collect and process mouse dynamics, and study the various factors used to build the user’s signature. The Authors will also provide an updated survey on the researches and industrial implementations related to the technology, and study possible applications in computer security.In this chapter, we investigate 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. We employ an efficient spatiotemporal representation for human repetitive activity and individual recognition, which represents human motion sequence in a single image while preserving spatiotemporal characteristics. A statistical approach is used to extract features for activity and individual recognition. Experimental results show that the proposed approach achieves good performance for repetitive human activity and individual recognition.

Security Evaluation of Behavioral Biometric Systems

For establishing trust 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 wide international recognition is desired. Within the common criteria, predefined security assurance requirements describe actions to be carried out by the developers of the product and by the evaluators. The assurance components that require clarification in the context of biometric systems are related to vulnerability assessment. This chapter reviews the state of the art and gives a gentle introduction to the methodology for evaluating the security of biometric systems, in particular of behavioral biometric verification systems.

Gait Recognition and Analysis

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. This chapter provides a survey of recent advances in gait recognition. First, an overview on gait recognition framework, feature extraction, and classifiers is given, and then some gait databases and evaluation metrics are introduced. Finally, research challenges and applications are discussed in detail.

Individual Identification from Video Based on "Behavioural Biometrics"

This chapter presents multiple methods for recognizing individuals from their “style of action/actions,” that is, “biometric behavioural characteristics.” Two forms of human recognition can be useful: the determination that an object is from the class of humans (which is called human detection), and the determination that an object is a particular individual from this class (which is called individual recognition). This chapter focuses on the latter problem. For individual recognition, this chapter considers two different categories. First, individual recognition using “style of single action,” that is, hand waving and partial gait, and second, individual recognition using “style of doing similar actions” in video sequences. The “style of single action” and “style of doing similar actions,” that is, behavioural biometric characteristics, are proposed as a cue to discriminate between two individuals. Nowadays, multibiometric security systems are available to recognise individuals from video sequences. Those multibiometric systems are combined with finger print, face, voice, and iris biometrics. This chapter reports multiple novel behavioural biometric techniques for individual recognition based on “style of single action” and “style of multiple actions” (i.e., analysing the pattern history of behavioural biometric motion), which can be additionally combined with finger print, face, voice, and iris biometrics as a complementary cue to intelligent security systems.