Method to evaluate pose variability in automatic face recognition performance

Authors:
Yednek Asfaw;Guy Scott;Paul Pelletier;Andy Adler
Affiliations:
Systems and Computer Engineering, Carleton University, Ottawa K1S 5B6, Ontario, Canada;Citizenship and Immigration Canada, Ottawa K1A 1L1, Ontario, Canada;Seacom Technologies Inc. Ottawa, Ontario, Canada;Systems and Computer Engineering, Carleton University, Ottawa K1S 5B6, Ontario, Canada
Venue:
International Journal of Biometrics
Year:
2012

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Abstract

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.