Reliability of Decision Support in Cross-spectral Biometric-enabled Systems

This paper addresses the evaluation of the performance of face and facial expression biometrics through a decision support system. The evaluation criteria include capturing the risk of the system, estimating the reliability of decision, and predicting the change in the perceived operator's trust in the decision. The relevant applications include human behavior monitoring and stress detection in individuals and teams, and in situational awareness system. Using an available database of cross-spectral videos of faces and facial expressions, we conducted a series of experiments to 1) demonstrate the phenomenon of biases in biometrics that affect the evaluated measures of the performance in human-machine systems, 2) explore the overall risk of the system caused by error rates such as false match and false nonmatch rates, and 3) calculate the reliability of cross-spectral and emotion-varying face identification.