Face Recognition Method Based On Wavelet Packet Transform And Two Dimensional Quaternion Principal Component Analysis

As one of hotspots in the field of pattern recognition and artificial intelligence, the research of face recognition has been drawn more and more attention in recent years. Principal component analysis (PCA) is a classical algorithm in face recognition with the character of that the process of recognition is simple and direct, but affected easily by illumination, facial expression and other factors; Quaternion principal component analysis (QPCA) is another face recognition algorithm, which can make full use of the structural information among pixels, but need high image preprocessing and often is referred to color face image recognition. Now a fact is that pictures collected in most people face databases are gray images. Considering two factors of recognition performance and computational complexity, this paper makes the improvements in feature extraction and recognition based on the PCA.Firstly, according to studying the theory of wavelet packet transform and quaternion, a new method of facial feature representation is proposed. Face images are decomposed by wavelet packet transform, which can provide multi-scale decomposition for a non-stationary signal. Each decomposition component is used to construct quaternion matrix, which can contain the global and detailed information of images, but also enhance the relationship between each component.Secondly, for quaternion principal component analysis has heavy work in calculation, two dimensional quaternion principal component analysis (2D-QPCA) is proposed to reduce dimensions and construct feature space. The space is divided into several sub-blocks, and each sub-block is classified based on nearest neighbor classification. The ultimate face recognition is completed according to classification results.Simulation experiments are carried on the face databases of ORL, Yale, YaleB and Indian to research the relationship of different parameters and recognition accuracy in the proposed method. Compared with PCA and other algorithms, experiment results show that the proposed face recognition method improves the robustness to illumination and expression changes, and is possessed the characteristics of high accuracy and low computational complexity.