Abstract: Aiming at the problem that extraction accuracy and speed were limited when using principal component analysis (PCA) algorithm only in face image high-dimensional data dimensionality reduction, we proposed a hybrid feature extraction algorithm based on wavelet transform and improved PCA. Firstly, the face image was decomposed by wavelet, and low-frequency component was selected for feature extraction. Secondly, the improved PCA algorithm was used for principal component extraction to obtain the feature vectors representing face features. Finally, the algorithm was applied to the image classification of Olivetti Faces  dataset. The experimental results show that the recognition accuracy is improved by 10% and the recognition speed is improved by about 37% when the image feature data processed by the hybrid algorithm are classified and recognized by convolutional neural network (CNN) algorithm.

Key words: face recognition, feature extraction, wavelet transform, principal component analysis (PCA)