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

TL;DR

This paper evaluates the reliability of decision support systems using face and facial expression biometrics, highlighting biases that impact performance and trust in human-machine interactions.

Contribution

It introduces an evaluation framework for biometric decision support systems and reveals biases affecting their reliability and operator trust.

Findings

Biases significantly influence biometric system performance.

Reliability of decision support varies with biometric biases.

Operator trust is affected by biometric decision accuracy.

Abstract

This paper addresses the evaluation of the performance of the decision support system that utilizes face and facial expression biometrics. The evaluation criteria include risk of error and related reliability of decision, as well as their contribution to the changes 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 that demonstrate the phenomenon of biases in biometrics that affect the evaluated measures of the performance in human-machine systems.