Artificial Neural Network Based Face Detection Using Gabor Feature Extraction

This paper proposes a classification-based face detection method using Gabor filter features. Considering the desirable characteristics of spatial locality and orientation selectivity of the Gabor filter, we design filters for extracting facial features from the local image. The feature vector based on Gabor filters is used as the input of the classifier, which is a Feed Forward neural network (FFNN) on a reduced feature subspace learned by an approach simpler than principal component analysis (PCA). The effectiveness of the proposed method is demonstrated by the experimental results on testing a large number of images and the comparison with the stateof-the-art method. The image will be convolved with Gabor filters by multiplying the image by Gabor filters in frequency domain. To save time they have been saved in frequency domain before Features is a cell array contains the result of the convolution of the image with each of the forty Gabor filters. The input vector of the network will have large values, which means a large amount of computation. So we reduce the matrix size to one-third of its original size by deleting some rows and columns. Deleting is not the best way but it save more time compare to other methods like PCA. Face detection and recognition has many applications in a variety of fields such as security system, videoconferencing and identification. The objective of this work is to implement a classifier based on neural networks (Multi-layer Perceptron) for face detection. The ANN is used to classify face and non-face patterns.