Evaluation of a facial recognition algorithm across three illumination conditions

This work evaluated the performance of a commercially available face recognition algorithm for the verification of an individualʹs identity pertaining to three enrollment illumination levels. Existing facial recognition technology from still or video sources is becoming a practical tool for law enforcement, security, and counter-terrorist applications despite the limitations of the current technology. At this time, facial recognition has been implemented in limited applications, but has not been exhaustively studied in adverse conditions, which has initiated continuing study aimed at improving algorithms to compare images or representations of images to recognize a suspect (Paul, 2002). Moreover, this evaluation examined the influence of variations in illumination levels on the performance of a face recognition algorithm, specifically testing the significance between verification attempts and enrollment conditions with respect to factors of age, gender, ethnicity, facial characteristics, and facial obstructions. The results of this evaluation showed that for low and medium illuminance enrollments, there was a statistically significant difference between verification attempts made at low, medium, and high illuminance. However, for the high illuminance enrollment, there was no statistically significant difference between verification attempts made at low, medium, or high illuminance. Furthermore, this evaluation showed that the enrollment illumination level is a better indicator of the verification rate than the verification illumination level.