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.
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A simple and effective source code for Face Identification based on DCT and Neural Networks. A List of Face Databases Available on the Web  When benchmarking an algorithm it is recommendable to use a standard test data set for researchers to be able to directly compare the results. While there are many databases in use currently, the choice of an appropriate database to be used should be made based on the task given (aging, expressions, lighting etc). Another way is to choose the data set specific to the property to be tested (e.g. how algorithm behaves when given images with lighting changes or images with different facial expressions). Click on link below to get details:
Index Terms: face, database, databases, download, list.
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