FACE DETECTION USING GABOR FILTERS AND NEURAL NETWORKS

In this paper, a robust method for face detection from different views using a combination of Gabor filters and neural networks is presented. First, a mathematical equation of Gabor filter is expressed. Then, by examining 75 different filter banks, range of effective parameters values in Gabor filter generation is determined, and finally, the best value for them is specified. The neural network used in this paper is a feed-forward back-propagation multilayer perceptron network. The input vector of the neural network is obtained from the convolution the input image and a Gabor filter with angles π / 2 and the frequency π / 2 in the frequency domain. The proposed method has been tested on 550 image samples from Feret database with simple background and Markus Weber database with complex background, and detection accuracy of them is 98.4% and95%, respectively. Also, the face area has been detected using Viola-Jones algorithm, and then comparison between the results obtained from Viola-Jones algorithm and the proposed method is described.