SINGLE SAMPLE RECOGNITION BASED ON LPP

For a single sample face recognition problems, this paper propose a supervised two-way two-dimensional locality preserving projection method to extract the facial features, based on locality preserving projection. The recognition phase, this paper presents a method of subcharacteristics voting, In this method, firstly, make face image into blocks, each subset of blocks as a sub-feature and statistics weights of each sub-feature, finally, identify by voting. Experimental results on YaleB and Yale show that the presented method can achieve a certain degree of recognition accuracy. Introduction Face recognition technology is one of the biometric identification technologies, the main purpose is identification by visual characteristics of the human face. Face recognition involves several research areas, such as image processing, machine vision, pattern recognition and so on. With the development of computer technology and the wide range of application requirements, face recognition technology has become a hot topic, and has made great progress. The main current methods of face recognition are geometric features, feature subspace, neural network method, elastic matching method, hidden Markov model method and so on. Subspace methods are PCA, LDA, ICA, etc.These are the traditional linear dimensionality reduction methods, and is good at global linear structure. Literature do principal component analysis for the face image training set, get feature subspace from transform matrix which is applied with singular value, project the training image and test image into subspace, then select classifier for face recognition. Literature extract feature subspace of image training sets using PCA, the training images and test images are projected in the subspace, the feature vectors are extracted and the statistical properties are calculated, adopt Bayesian decision based on minimum error rate to realize face recognition. Literatur propose an improved method which do 2DPCA reconstruction in class, deal the 2-dimensional image matrix directly, avoiding the high computational complexity which is caused by stretching the 1-dimensional vector. But the face features are usually characterized by a high nonlinear dimensionality reduction, using these methods will lost the intrinsic relationship hidden in the data. The study showed that the high nonlinear structures have low-dimensional manifold, manifold learning theory can solve nonlinear problems, and has been successfully applied. Rowels and Saul proposed locally linear embedding (LLE algorithm, which can be mapped high-dimensional data point into a global low-dimensional coordinate system, is still maintained after the near-relational mapping high-dimensional, so that we can effectively retain the nonlinear structure of data, but also translational and rotational invariance. Literature combine the linear subspace method and manifold learning method, assign a new face recognition algorithm: Local Maximum Margin Linear Distinct Embedding, but the identification result is not good when apply single subspace analysis into large-scale face database. Tenenbaum et proposed isometric mapping (ISOMAP) algorithm, which will build a nearest neighbor graph by data points, the shortest route instead of Euclidean distance, and then after multidimensional scaling analysis (MDS) process, get low-dimensional coordinates which can be embeddable in high-dimensional space. On this basis, there are some improved algorithms, such as Laplace feature mapping algorithm (LE), the Hessian LLE algorithm (HLLE), the local tangent International Power, Electronics and Materials Engineering Conference (IPEMEC 2015) © 2015. The authors Published by Atlantis Press 1069 space sorting algorithm (LTSA), etc. Traditional manifold learning algorithm does not get projection matrix from high-dimensional to low-dimensional, so extracting features directly from new samples is difficulty. He et put forward a locality preserving projections algorithm (LPP) to solves this problem. This article is based on LPP features, and improved projection matrix, using bi-dimensional supervised LPP method to extract features of face images and using child features of the proposed voting method to classify, after experimental verification, the method in the paper is more effective than LPP method. LPP The basic idea of LPP is to obtain an transformation matrix, can project high-dimensional data set onto a low-dimensional data set, and ensure that adjacent data in original data set are also adjacent to each other in projection data set. Algorithm as follows: Construction of the adjacent map. The map can be an weighted undirected graph with nodes. There are two ways to determine whether there is a connection between the points i and j : one is ε near, that is, the distance between i x , j x meet 2 i j x x ε − < ; another method is k near, that is, node i is one of the k adjacent nearest nodes of node j , or node j is one of the k nearest adjacent nodes of node i . Dtermine the edge value matrix S of the edge in adjacent diagram G . When there is no edge between the points i and j , the weight value is 0,otherwise, the weight calculation methods are: one is simple, that is 1 ij S = , another one is the thermonuclear function, that is