Comparison Of Feature Extraction Algorithms For Gender Classification From Face Images

Gender Classification is the hot research topic from last two decades but still a gap exist between the requirements and actual performances. This gap lies due to the variation in pose, expression and illumination condition etc. Gender classification of face images is the process of identification of gender by their facial images. In this paper we compared the performance of two feature extraction algorithm i.e. Local binary pattern (LBP) and Histogram of oriented gradient (HOG) in order to determine the more efficient approach for gender classification from face images. Haar Cascade Classifier is used for the face detection from an image. Histogram equalization normalization technique is used for normalizing illumination effects. Support vector machine (SVM) is used as a classifier for gender classification. We implement gender classification system architecture using OpenCv 2.4.2. Indian face database (IFD) is used for the experiment . Experimental results on Indian face database show that HOG is more efficient approach for gender classification and improves gender recognition rate upto 95.56%. KeywordsGender classification, Haar cascade classifier, Histogram of oriented gradient, Local binary pattern, Support vector machine.

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