Face Detection Based on Skin Color Using Neural Networks

Face detection is one of the challenging problems in image processing. A novel face detection system is presented in this paper. The system combines two algorithms for face detection to achieve better detection rates. The two algorithms are skin detection and neural networks. In the first module of the system a skin color model based on normalized RGB color space is built and used to detect skin regions. The detected skin regions are the face candidate regions. In the second module of the system, the neural network is created and trained with training set of faces and non-faces. The network used is a two layer feed-forward network. There are two modifications for the classical use of neural networks in face detection. First, the neural network tests only the face candidate regions for faces, thus the search space is reduced. Second, the window size used by the neural network in scanning the input image is adaptive and depends on the size of the face candidate region. This enables the face detection system to detect faces with any size. In experiments on images having upright frontal faces with any background our system has achieved high detection rates and low false positives.