Multiscale Feature and Single Neural Network Based Face Recognition

This research paper deals with the implementation of face recognition using neural network (recognition classifier) on multi-scale features of face (such as eyes, nose, mouth and remaining portions of face). The proposed system contains three parts, preprocessing, multi scale feature extraction and face classification using neural network. The basic idea of the proposed method is to construct facial features from multi-scale image patches for different face components. The multi-scale features of face (such as Eyes, Nose, Mouth and remaining portion of face) becomes the input to neural network classifier, which uses back propagation, algorithm and radial function network to recognize familiar faces (trained) and faces with variations in expressions, illumination changes, tilt of 5 to 10 degrees and with spects. The crux of proposed algorithm is its beauty to use single neural network as classifier, which produces straight forward approach towards face recognition. The proposed algorithm was tested on FERET face data base for 200 images of 40 subjects (120 faces for training 80 for testing) and results are encouraging compared to other face recognition techniques. (1, 2, 3)