A Image Decomposition Method Based on Euler Mapping for Face Recognition

Abstract

This paper presents a robust but simple image feature representation method, called image decomposition based on Euler mapping (IDEM). IDEM firstly captures the orientation information by implementing arctangent operator for each pixel. Then, the orientation image is decomposed into two mapping images by executing Euler mapping. Each mapping image is normalized using the “z-score” method, and all normalized vectors are concatenated into an augmented feature vector. The dimensionality of the augmented feature vector is reduced by linear discriminant analysis to yield a low-dimensional feature vector. Experimental results show that IDEM achieves better results in comparison with state-of-the-art methods.