Open AccessAge and Gender Prediction using Face Recognition
Author(s) -
Sai Teja Challa,
AUTHOR_ID,
Sowjanya Jindam,
Ruchitha Reddy Reddy,
Kalathila Uthej,
AUTHOR_ID,
AUTHOR_ID,
AUTHOR_ID
Publication year - 2021
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.b3275.1211221
Subject(s) - convolutional neural network , deep learning , artificial intelligence , computer science , benchmark (surveying) , face (sociological concept) , facial recognition system , machine learning , pattern recognition (psychology) , architecture , range (aeronautics) , engineering , art , social science , geodesy , aerospace engineering , sociology , visual arts , geography
Automatic age and gender prediction from face images has lately attracted much attention due to its wide range of applications in numerous facial analyses. We show in this study that utilizing the Caffe Model Architecture of Deep Learning Frame Work; we were able to greatly enhance age and gender recognition by learning representations using deep-convolutional neural networks (CNN). We propose a much simpler convolutional net architecture that can be employed even if no learning data is available. In a recent study presenting a potential benchmark for age and gender estimation, we show that our strategy greatly outperforms existing state-of-the-art methods.