Neural network technology for pattern recognition, stock prediction and market forecasting

High Speed Face Recognition Based on Discrete Cosine Transforms and Neural Networks

Download now Matlab source code
Requirements: Matlab, Matlab Image Processing Toolbox, Matlab Neural Network Toolbox.

High information redundancy and correlation in face images result in inefficiencies when such images are used directly for recognition. In this paper, discrete cosine transforms are used to reduce image information redundancy because only a subset of the transform coefficients are necessary to preserve the most important facial features such as hair outline, eyes and mouth. We demonstrate experimentally that when DCT coefficients are fed into a backpropagation neural network for classification, a high recognition rate can be achieved by using a very small proportion of transform coefficients. This makes DCT-based face recognition much faster than other approaches.

Zhengjun Pan and Hamid Bolouri, "High Speed Face Recognition Based on Discrete Cosine Transforms and Neural Networks", 1999.

Index Terms: Face recognition, neural networks, feature extraction, discrete cosine transform, face matching, face identification, dct, ann, artificial neural networks, nn.

Release 1.0 Date 2006.05.16
Major features:

• Face recognition based on DCT (Discrete Cosine Transforms) and Neural Networks
• Easy and intuitive GUI
• Demo code (protected P-files) available for performance evaluation