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Gender Classification Using One Half Face and Feature Selection Based on Mutual Information

One important application of biometrics is determining the gender and age of customers so that attention could be specialized to improve sales. Gender classification has been a common topic of research and given the existence of face symmetry, determining gender based on half of the face seems feasible to significantly reduce computational cost. In this paper, we report the exploration of using the symmetrical characteristics of the face for representing and determining gender from only half of the face. We first divide the faces into two halves, and then select the best features separately from the left and right sides. The method uses 4 different mutual information measures to select features, minimum redundancy and maximal relevance (mRMR), normalized mutual information feature selection (NMIFS), conditional mutual information feature selection (CMIFS), and conditional mutual information maximization (CMIM). We tested our method on the FERET database using 5 fold cross-validation. It is shown that selection of features significantly improved gender classification accuracy compared to the use of full faces. We also show a significant reduction in processing time making real-time applications feasible.