Gender Classification Based on Local Gabor Binary Mapping and Support Vector Machine
LV Bao-liang · Jisuanji gongcheng · 2009
Multi-view gender classification,which is based on facial images,is one of the most challenging problems for computer vision researchers.This paper proposes a novel approach,Local Gabor Binary Mapping Pattern(LGBMP),to improve the correct classification rate for multi-view gender classification.The proposed approach which combines local binary pattern histogram,spatial information and the magnitude part of Gabor filter is robust to noise and local image transformations caused by variations of illumination and pose.Experimental results on the CAS-PEAL face database show that the proposed LGBMP achieves the highest correct classification rate of 95% on all of the 9 face poses.