The Feature Analysis Of High Dimensional Data In Face Recognition

In recent years, the face recognition has attracted considerable attention within the community of pattern recognition. As one of the most successful branches of biometrics, it has great potential applications in finance security, electronic commerce, and digital entertainment, etc. Over the past half of century, the face recognition has developed rapidly. Now under the controlled conditions, face recognition systems have achieved good results. However, a great number of challenges are still leaved to resolve before one can implement a robust and practical face recognition application. Among these challenges, the large-scale data storage and computation arising from excessively high face data is one of the most difficult.Our work is focusing on the dimension reduction of face data and recognition problem. The work and the innovation in this dissertation can be summarized as following.(1) The dimensionality reduction problem of face data based on typical linear and nonlinear dimensionality reduction algorithms is investigated. Meanwhile, the performance of these algorithms is intensively analyzed. Based on the residual variance evaluation model, this dissertation discusses the intrinsic dimension of facial images. Following that, the influences of neighborhood parameters k on dimensionality reduction are taken into account. Experiments on the ORL, Yale, and Feret face database show the performance of nonlinear dimensionality reduction algorithms is better than that of linear ones.(2) The face recognition method based on the nonlinear dimensionality reduction algorithm is studied. Firstly, this dissertation introduces the incremental Isomap algorithm to resolve the novel samples'mapping and dimensionality reduction problem in the training space. Following that, a face recognition method based on IADP-Isomap is proposed. The experimental results show that the recognition method is feasible.(3) The non-negative matrix factorization (NMF) algorithm and its application in face recognition is discussed. On condition of the variation of illumination, poses, and expression, the performance of NMF-based recognition method would dramatic decreases. Focusing on this problem, this dissertation proposes a so-called NMF+SDA algorithm. It can effectively implement dimensionality reduction and feature extraction of the face dada. Experiments on face database exhibit that NMF+SDA owns better recognition rates than traditional NMF.(4) Based on the nonlinear dimensionality reduction algorithm, the concepts of vector membership function and membership degree in fuzzy mathematics are introduced. It is presented that the fuzzy matching for a nonlinear function between input and output can be realized by using three rulers (two point rulers and one slope ruler). The affinity between two memberships can be used for assessment to the linearity of the matched curve. Consequently, the algorithm of polynomial fuzzy matching based on three rulers is proposed and applied in face recognition. Experimental results demonstrate the recognition algorithm is feasible and has good recognition capability.