Computer Vision-Based Gender Detection from Facial Image

Abstract

face detection, object detection, image processing Abstract � Computer vision-based gender detection from facial images is a challenging and important task for computer vision-based researchers. The automatic gender detection from face images has potential applications in visual surveillance and human-computer interaction systems (HCI). Human faces provide important visual information for gender perception. This research presents a novel approach for gender detection from facial images. The system can automatically detect face from input images and the detected facial area is taken as region of interest (ROI). Discrete Cosine Transformation (DCT) of that ROI plays an important role in gender detection. A gender knowledgebase of the processed DCT is created utilizing supervised learning. To detect gender, input image is passed through a classifier which is based on that knowledgebase. To improve the matching accuracy Local Binary Pattern (LBP) of the ROI is done before converting it into DCT. This research has experimented on a database of more than 4000 facial images which are mainly of this subcontinent in order to evaluate the performance of the proposed system. The average accuracy rate achieved by the system is more than 78%. 1