Face Recognition Method Based on Multi-Level Histogram Sequence of Gabor Fused Features

Abstract

In this paper, the Gabor fused features are combined with multi-level histogram sequence to extract facial features in order to overcome the disadvantage of traditional Gabor filter bank, whose high-dimensional Gabor features are redundant and the global features representation capacity is poor. First, we get the standard face by face detection, eyes location, geometric normalization and illumination normalization. Second, to extract the multi-orientation information and reduce the dimension of the features, a fusion rule is proposed to fuse the original Gabor features of the same scale into a single feature, and then the fused image will be divided into multi-level changeable units, and the histogram of each unit is computed and combined as facial features. Experimental results on ORL via MATLAB show an encouraging performance for face recognition.