Extended multi-spectral imaging for gender classification based on image set

Abstract

Gender prediction based on facial features has received significant attention in computer vision and biometric community. Most of the gender classification studies mainly focused there attention on approaches that operate in the visible spectrum. In this paper we present gender classification using extended multi-spectral face data captured in nine narrow spectral bands across the visible near infrared spectrum (530nm to 1000nm). Further, we present the proposed method, that learns for this image set the discriminative spectral band features in the affine space and then classifies the features with a Support Vector Machine (SVM) in a robust manner. The extensive experimental results are presented on the reasonable sample size of 78300 spectral band images using our proposed method. The obtained results shows 90.49 plus or minus 3.56 percent average classification accuracy, indicating the applicability of our proposed method for gender classification.