Method to evaluate pose variability in automatic face recognition performance
A key concern in Automatic Face Recognition (AFR) is the decrease of recognition performance as the quality of images decreases. This paper introduces a method to evaluate the impact of face pose variability on face recognition accuracy. Experiments were conducted using three leading commercial face recognition algorithms on data with poses from 0 to ±20 deg in each of the roll, pitch, and yaw directions per subject. Results indicate that roll variations has small effect on performance, while pitch and yaw variations produce a significant increase in error rates. More recent algorithms show better results at low pose variability.
|Keywords||AFR, Automatic face recognition, Biometric performance analysis, Biometric sample quality, Receiver operator curve|
|Journal||International Journal of Biometrics|
Asfaw, Y. (Yednek), Scott, G. (Guy), Pelletier, P. (Paul), & Adler, A. (2012). Method to evaluate pose variability in automatic face recognition performance. International Journal of Biometrics, 4(4), 373–387. doi:10.1504/IJBM.2012.049734