A Human Visual Experience Inspired Similarity Metric for Face Recognition Under Occlusion
AbstractBackground/IntroductionRecognizing an occluded face is a challenging task for face recognition systems. Although many methods for dealing with occlusion have been proposed, it is more attractive to build a robust face recognition system that focuses on non occluded regions. Such systems automatically ignore occluded parts, which is broadly consistent with the human visual experience. MethodsBased on this idea, a new similarity metric called the average degree of aggregation of matched pixels (ADAMP) is proposed. The discrimination performance of ADAMP is derived from information about the spatial distribution of matched pixels.ResultsThe proposed method is evaluated with extensive experiments. Compared with state of the art methods, our method is very competitive in terms of recognition accuracy and computation time. In particular, recognition rates of 99.5 in the presence of sunglasses and 96.5 in the presence of scarves can be achieved on a benchmark dataset.ConclusionsAlthough ADAMP is relatively simple and has the same time complexity as the Euclidean distance, it is demonstrated to be very robust against occlusion. Recognition results using ADAMP are very competitive with those given by state of the art methods.
Cognitive Computation