Algorithms For Face Recognition Based On Principal Component Analysis

With the development of information technology, information security awareness is growing. In the business world, military field, network security, and other fields,identification requirements become more and more strict. Face recognition as one of the difficulties and hotspot in the field of biological recognition, it has important theoretical research value and practical application. These reasons make face recognition got swift and violent development, In this paper, the present study focuses on how to be more efficient and convenient for face classification.At first, this paper simply introduces several face image preprocessing methods, then introduced in detail based on traditional principal component analysis (PCA) and two-dimensional principal component analysis (2DPCA) face recognition, analyzed their advantages and disadvantages. In this paper, on the basis of the 2DPCA and at the same time introduces the ideas of chunking and two-way feature extraction proposes a new recognition algorithm:bidirectional modular two-dimensional principal component analysis (BM2DPCA), then we combined BM2DPCA with the Discrete Cosine Transform (DCT) to form a new face recognition algorithm. At first, the DCT and EDCT was used for dimension reduction and image reconstruct, then we can from plane and upright orientation with BM2DPCA method for feature extraction. Finally, the Nearest Neighborhood (NN) algorithm was used to construct classifiers.In this paper, we do some basic research on face recognition, improved the traditional recognition methodon, and through experiment verification has reached the expected purpose. Simulation results show that no matter from the human face reconstruction effect or the size of the recognition rate of this algorithm is superior to the traditional PCA and 2DPCA and DCT-B2DPCA. And the algorithm of the characteristic matrix dimension values has good robustness.