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Multiscale local discriminatory feature representations for automatic face recognition

In automatic face recognition, strong discriminatory feature extraction is very important. In this paper a new approach to extract powerful local discriminatory features is introduced. Instead of using traditional wavelet features, the authors examine multiscale local statistical characteristics to achieve strong discriminatory features based on important wavelet subbands. Meanwhile, to efficiently utilize potentials for the extracted multi- MLDFs, an integrated recognition system is developed, where multi-classifiers first conduct the corresponding coarse classification, then a decision fusion scheme by associating different priorities with each of the classifiers makes the final recognition. Our experiments showed this technique achieves superior performance to popular methods such as PCA/Eigenface, HMM, wavelet features, and neural networks, etc.