Report on the Evaluation of 2D Still-Image Face Recognition Algorithms

Abstract

The paper evaluates state-of-the-art face identification and verification algorithms, by applying them to corpora of face images the population of which extends into the millions. Performance is stated in terms of core accuracy and speed metrics, and the dependence of these on population size and image properties are reported. One-to-many search algorithms are evaluated in terms of their use in both investigational and identification modes. Investigational performance has implications for workload on an examiner reviewing the results of a search. Identification performance, using a high score threshold, can support fully automated operation and decision making if some quantified level of false match is acceptable. In addition, the paper establishes an initial approach toward calibration of false match accuracy.