CNN based features extraction for age estimation and gender classification

Abstract

In recent years, age estimation and gender classification was one of the issues most frequently discussed in the field of pattern recognition and computer vision. This paper proposes automated predictions of age and gender based features extraction from human facials images. Contrary to the other conventional approaches on the unfiltered face image, in this study, we show that a substantial improvement be obtained for these tasks by learning representations with the use of deep convolutional neural networks (CNN). The feedforward neural network method used in this research enhances robustness for highly variable unconstrained recognition tasks to identify the gender and age group estimation. This research was analyzed and validated for the gender prediction and age estimation on both the Essex face dataset and the Adience benchmark. The results obtained show that the proposed approach offers a major performance gain, our model achieve very interesting efficiency and the state-of-the-art performance in both age and gender scoring.