Gender and Age Detection using Deep Learning

Abstract

Most applications now require automatic gender and age prediction, especially with the advent of social media and social platforms. Furthermore, existing technologies' performance on actual imageries is pointedly absent, exclusively when associated to the mammoth rises in performance freshly testified for the related endeavor of facial recognition. In this paper, we demonstrate how deep-CNN can be used to learn representations (CNN). The five phases of the predicted technique are facial recognition, environment removal, face alignment, numerous CNN, and voting systems. This model is tested on the recent Audience-Face benchmark face dataset for gender detection and age approximation, and it is implemented using Python software.