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Multi-view Gender Classification using hybrid Transformed Features

In this paper, a two step efficient pose based gender classification technique has been presented. In first step, the proposed technique uses different type of features for pose classification which classifies the input image as frontal or side pose image. Based on pose classification result, gender classification is performed in the second step. Two different types of gender classifiers have been proposed for both side pose and frontal images which use the image and pose classification result as input and classifies the image as male or female. The proposed technique is tested over standard datasets using well known quantitative measures. Results show that the proposed technique gives superior performance than the existing techniques.