Face Detection using Neural Network & Gabor Wavelet Transform

This paper is based on classifications of the features of a face detected using Gabor filter feature extraction techniques in image processing. The feature vector based on Gabor filters 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 large number of images and comparisons with state of the art method. Face detection and recognition has many applications in a variety of fields such as security systems, video conferencing and identification. Human face detection and recognition is an active area of research spanning several disciplines such as image processing, pattern recognition and computer vision. Face detection and recognition are preliminary steps to a wide range of applications such as personal identity verification, video-surveillance, lip tracking, facial expression extraction, gender classification, advanced human and computer interaction. Most methods are based on neural network approaches, feature extraction, Markov chain, skin color, and others are based on template matching (1). Pattern localization and classification is the step, which is used to classify face and non- face patterns. Many systems dealing with object classification are based on skin color. In this paper we are interested by the design of an ANN algorithm in order to achieve image classification. This paper is organized as follows: In section II, we give an overview over classification for face detection. Description of our model is discussed in Section III. Section Ideals with the training method.