Intelligent Gender Recognition System for Classification of Gender in Malaysian Demographic

Abstract

Identification of a person gender as a man or woman based on the past experiences through features of face such as eyes, mouth, cheek can be obtained through an intelligent gender recognition system. Detection of a person’s gender can be difficult but important for security purposes, especially where safety issues concerning woman in public amenities. The objectives of this research are to identify the techniques for classifying features from man and woman facial images, through which embed as a system and validify using photos within Malaysian demographic. This research is focused on utilizing facial features for gender classification in real time, emphasizing on deep learning-based gender recognition and HAAR Cascade classifier using pre-trained caffe model in OpenCV library. Results show that under Malaysian demographic, probability of 86% accuracy of gender recognition were obtained.