Age and Gender Detection Using CNN

Abstract

In recent years, much effort has been put forth to balance age and sexuality. It has been reported that the age can be accurately measured under controlled areas such as front faces, no speech, and stationary lighting conditions. However, it is not intended to achieve the same level of accuracy in the real world environment due to the wide variation in camera use, positioning, and lighting conditions. In this paper, we use a recently proposed mechanism to study equipment called covariate shift adaptation to reduce the change in lighting conditions between the laboratory and the working environment. By examining actual age estimates, we demonstrate the usefulness of our proposed approach.