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Face images are essential to intelligent human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, expression recognition and gesture recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image or a sequence of images, the goal of face detection is to identify all image regions which contain a face regardless of its three-dimensional position and orientation and lighting conditions. Such a problem is challenging because faces are non-rigid and have a high degree of variability in size, shape, color, and texture. Numerous techniques have been developed to detect faces in an image or an image sequence, and the purpose of this paper is to discuss and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.
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