Modeling and Predicting Face Recognition System Performance Based on Analysis of Similarity Scores

—This paper presents methods of modeling and predicting face recognition (FR) system performance based on analysis of similarity scores. We define the performance of an FR system as its recognition accuracy, and consider the intrinsic and extrinsic factors affecting its performance. The intrinsic factors of an FR system include the gallery images, the FR algorithm, and the tuning parameters. The extrinsic factors include mainly query image conditions. For performance modeling, we propose the concept of “perfect recognition,” based on which a performance metric is extracted from perfect recognition similarity scores (PRSS) to relate the performance of an FR system to its intrinsic factors. The PRSS performance metric allows tuning FR algorithm parameters offline for near optimal performance. In addition, the performance metric extracted from query images is used to adjust face alignment parameters online for improved performance. For online prediction of the performance of an FR s...

• Type: Journal
• Year: 2007
• Where: PAMI
• Authors: Peng Wang, Qiang Ji, James L. Wayman
• Keywords: FR Algorithm, PAMI 2007, Performance, Query Image