Face detection using CMAC neural network

Abstract

We present a new method based on CMAC neural network, used as classifier in a frontal face detection system. The gray level and the position of the pixels of an input image are directly presented to the network. Due to the simple structure of CMAC, with only one trainable layer, the training phase is very fast. The proposed method has been tested on a data set containing 960 faces and 20000 non-faces, selected among difficult face and non-face patterns. The results of experimentations exhibit an error rate of 8.5%, which is a reasonable result considering the simple structure of system and the important number of difficult patterns in the test dataset.