A Framework for Performance Evaluation of Face Recognition Algorithms

Abstract

Face detection and recognition is becoming increasingly important in the contexts of surveillance, credit card fraud detection, assistive devices for visual impaired, etc. A number of face recognition algorithms have been proposed in the literature. The availability of a comprehensive face database is crucial to test the performance of these face recognition algorithms. However, while existing publicly-available face databases contain face images with a wide variety of poses angles, illumination angles, gestures, face occlusions, and illuminant colors, these images have not been adequately annotated, thus limiting their usefulness for evaluating the relative performance of face detection algorithms. For example, many of the images in existing databases are not annotated with the exact pose angles at which they were taken. In order to compare the performance of various face recognition algorithms presented in the literature there is a need for a comprehensive, systematically annotated database populated with face images that have been captured (1) at a variety of pose angles (to permit testing of pose invariance), (2) with a wide variety of illumination angles (to permit testing of illumination invariance), and (3) under a variety of commonly encountered illumination color temperatures (to permit testing of illumination color invariance). In this paper, we present a methodology for creating such an annotated database that employs a novel set of apparatus for the rapid capture of face images from a wide variety of pose angles and illumination angles. Four different types of illumination are used, including daylight, skylight, incandescent and fluorescent. The entire set of images, as well as the annotations and the experimental results, is being placed in the public domain, and made available for download over the worldwide web. 1