This paper proposes an improved version of our previously introduced face detection system based on skin color segmentation and neural networks. The new system, using a support vector machine (SVM) based method for learning and verification, consists of several stages. First, the system searches for the regions where faces might exist by using skin color information and forms a so-called skin map. After performing noise removal and some morphological operations on the skin map, it utilizes the aspect ratio of a face to find out possible face blocks, and then eye detection is carried out within each possible face block. If an eye pair is detected in a possible face block, a region is cropped according to the location of the two eyes, which is called a face candidate; otherwise, it is regarded as a non-face block. Finally, each of the face candidates is verified by a support vector machine. Experimental results reflect that the new version improves the verification accuracy of the previously proposed system.
Relation:
Far East Journal of Experimental and Theoretical Artificial Intelligence 3(2), pp.113-123
