Gender recognition using innovative pattern recognition techniques

The vast number of researchers has been focused on pattern recognition and computer vision fields in parallel with recent technological developments over the last two decades. Some of the topics in these areas are; face detection, face recognition and gender recognition. Mostly because, the studies conducted on these areas use native ways to collect biometric data without causing any inconvenience to the subject with their contactless and free flow nature. In this paper, a new system that provides gender information using facial images is presented. The system consists of two main stages; (i) face detection and (ii) gender recognition. In the first stage, the system focuses on the detection of frontal human faces in digital images. We used a linear classifier combined with Histogram of Oriented Gradients (HOG) feature for face detection. In the second stage, two different classifiers for gender recognition were trained. The first classifier is based on Support Vector Machines (SVM) and the second is based on Convolutional Neural Networks (CNN) which is also known as Deep Learning. We used Local Binary Pattern (LBP) and HOG as features for SVM classifier, and Radial Basis Function (RBP) as its kernel. For the CNN classifier, we used GoogleNet deep neural network architecture and the optimization was performed depending on the parameters. For training of both classifiers, Labeled Faces in the Wild (LFW), IMDB and WIKI data sets were used. In our experiments, we observed that the CNN based classifier surpasses the SVM based one in terms of accuracy.