Emerging Behavioral and Multi-Modal Biometric Approaches

Abstract

Emerging behavioral biometrics play a vital role in situations where traditional biometrics may fail to identify the person correctly. This thesis focuses on designing new methods for biometric identification systems using emerging behavioral biometric characteristics such as EEG brain waves and Kinect gait. This research also develops multimodal biometric methods using the combination of emerging behavioral biometric characteristics with physiological biometric characteristics (such as face). The sub-goal of this research is to establish a relationship between a person's mental activity and a system identification accuracy. To fulfill this goal, this research studies overt and covert EEG signals. The second emerging behavioral biometric system is Kinect skeletal gait, where novelty of the work is in considering all possible joint-distance combinations. Finally, two multimodal systems are proposed and developed using the score-level fusion. The performance of the proposed identification systems is evaluated using five publicly available databases.