Local Manifold Matching for Face Recognition

Abstract — In this paper, we propose a novel classification method, called local manifold matching (LMM), for face recognition. LMM has great representational capacity of available prototypes and is based on the local linearity assumption that each data point and its k nearest neighbors from the same class lie on a linear manifold locally embedded in the image space. We present a supervised local manifold learning algorithm for learning all locally linear manifold structures. Then we propose the nearest manifold criterion for the classification in which the query feature point is assigned to the most matching face manifold. Experimental results show that kernel PCA incorporated with the LMM classifier achieves the best face recognition performance. A. Locally Linear Assumption The key issue of nearest feature classifiers (NFL) is that how and where to generate virtual prototype feature points. Since NFL uses a linear model to generate an infinite number of virtual prototypes, We think the virtual prototypes should be created in the patch which is linear or close to linear. I