Gender Prediction from Images Using Deep Learning Techniques - Bhat, Salma Fayaz; Lone, Ab Waheed; Dar, Taniya Ashraf (IEEE, 2019)
Mechanized human age estimation by means of facial pictures is a vital and testing errand and has bunches of potential in certifiable applications, especially since the ascent of social stages and online networking. Hearty face affirmation structures are in unbelievable demand to help fight bad behavior and mental mistreatment. Distinctive applications join giving customer affirmation to get the opportunity to control to physical and virtual spaces to ensure higher security. In any case the issue of recognizing a man by taking a data stand up to picture and organizing with the known face pictures in a database is now an amazingly troublesome issue. This is a result of the variability of human faces under different operational circumstance conditions. For instance edification, insurgencies, appearances, camera see centers, developing, beauty care products, and eyeglasses. In this paper, a significant increase in accuracy of Gender prediction can be obtained through the use of convolution neural networks (CNN) for extracting features. Making the use of Convolution neural network (CNN) encompassed with deep learning methods, state-of the art performance has been achieved. The image-based Gender estimation is determined by performing extensive experiments on the largest public available datasets of face images with Gender labels-IMDB-WIKI dataset.
Keywords: Biometric; CNN; Epoc; Deep Learning; Image; Computer Science