Deep learning using bayesian optimization for facial age estimation

Abstract

Age Estimation plays a significant role in many real-world applications. Age estimation is a process of determining the exact age or age group of a person depending on his biometric features. Recent research demonstrates that the deeply learned features for age estimation from large-scale data result in significant improvement of the age estimation performance for facial images. This paper propose a Convolutional Neural Network (CNN) - approach using Bayesian Optimization for facial age estimation. Bayesian Optimization is applied to minimize the classification error on the validation set for CNN model. Extensive experiments are done for evaluating Deep Learning using Bayesian Optimization (DLOB) on three datasets: MORPH, FG-NET and FERET. The results show that using Bayesian Optimization for CNN outperforms the state of the arts on FG-NET and FERET datasets with a Mean Absolute Error (MAE) of 2.88 and 1.3, and achieves comparable results compared to the most of the state-of-the-art methods on MORPH dataset with a 3.01 MAE.