Introduction - If you have any usage issues, please Google them yourself
The method uses the natural characteristics of mirror symmetry of human face, generates mirror odd and even symmetrical samples according to the principle of parity decomposition, uses the symmetrical images of human face as training samples, then uses principal component analysis ( PCA ) to carry out second-order correlation and dimensionality reduction processing on the training samples, and then carries out ICA feature extraction on the processed samples. Theoretical and analytical experiments show that the algorithm can effectively reduce the face by the angle of view, lighting, facial expressions, posture changes and other factors, and increase the capacity of training samples, reduce the computational complexity, and effectively solve the problem of small samples, improve the recognition rate.