Skip to Main Content
In this paper, a novel method is proposed to detect faces based on PCNN time signature and skin color segmentation, in which no training is needed. A test image is first divided into overlapped blocks and extracted PCNN time signature as the detection features, which a two-dimensional image is projected to a one-dimensional feature space. The test blocks are matched to a face template, which can be a random face, not related to the test faces, based on Euclidean distance threshold. The candidate face blocks are clustered to determine the face number included in a probe image and the size of the face areas. Skin color segmentation is used to reduce the search areas and to speed up the detection procedure. The method demonstrates successful face detection over a wide range of facial variations in scale, resolution, facial expressions, with or without glasses, in the presence of various illumination conditions as well as under complex background from outdoor photo collections. The simulation result proves that the method is translation, rotation and scale invariant as well as insensitive to illumination changes.