HUMAN GENDER AND AGE RECOGNITION USING DEEP LEARNING METHODS

Abstract

In these modern days, age and gender recognition have become relevant to an increasing number of applications, particularly since the rise of social platforms and social media. Nevertheless, the performance of existing methods on real-world images is still significantly lacking. To solve such a delicate problem several handy approaches are being studied in Computer Vision. However, most of these approaches hardly achieve high accuracy and precision. Lighting, illumination, proper face area detection, noise, ethnicity, and various facial expressions hinder the correctness of the research. Although age and gender recognition can be accomplished using different biometric traits, this thesis is focused on facial age and gender recognition that relies on biometric features extracted from a person’s face. The main issues presented in the thesis involve typical applications where facial age and gender recognition can be used, problems and challenges associated with facial age and gender recognition, typical approaches reported in the literature, and future research directions. In this thesis, I would like to introduce and implement age and gender recognition with OpenCV and Deep Learning. Then, I will review the results and discuss the next steps.