Automatic Face Recognition with Well-Calibrated Confidence Measures

Abstract

Automatic face recognition (AFR) has gained the attention of many institutes and researchers in the past two decades due to its wide range of applications. This attention resulted in the development of a variety of techniques for the particular task with a high recognition accuracy when the environment is well-controlled. In the case of moderately controlled or fully uncontrolled environments however, the performance of most techniques is dramatically reduced due to the much higher difficulty of the task. As a result, the provision of some kind of indication of the likelihood of a recognition being correct is a desirable property of AFR techniques in many applications, such as the detection of wanted persons or the automatic annotation of photographs. This work investigates the application of the conformal prediction (CP) framework for extending the output of AFR techniques with well-calibrated measures of confidence. In particular we combine CP with one classifier based on patterns of oriented edge magnitudes descriptors, one classifier based on scale invariant feature transform descriptors, and a weighted combination of the similarities computed by the two. We examine and compare the performance of five nonconformity measures for the particular task in terms of their accuracy and informational efficiency.