Face detection system based on MLP neural network

Authors:
Nidal F. Shilbayeh;Gaith A. Al-Qudah
Affiliations:
Computer Science Department, Middle East University, Amman, Jordan;Computer Science Department, Middle East University, Amman, Jordan
Venue:
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
Year:
2010

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Abstract

Face detection is the problem of determining whether there are human faces in the image and tries to make a judgment on whether or not that image contains a face. In this paper, we propose a face detector using an efficient architecture based on a Multi-Layer Perceptron (MLP) neural network and Maximal Rejection Classifier (MRC). The proposed approach significantly improves the efficiency and the accuracy of detection in comparison with the traditional neural-network techniques. In order to reduce the total computation cost, we organize the neural network in a pre-stage that is able to reject a majority of nonface patterns in the image backgrounds, thereby significantly improving the overall detection efficiency while maintaining the detection accuracy. An important advantage of the new architecture is that it has a homogeneous structure so that it is suitable for very efficient implementation using programmable devices. Comparisons with other state-of-the-art face detection systems are presented. Our proposed approach achieves one of the best detection accuracies with significantly reduced training and detection cost.