Abstract
This paper proposes automatic age and gender predictions based on feature extraction from human facial images. In contrast to the other traditional methods on the unfiltered benchmarks show their failure to manage large degrees of variation in these types of facial images. In this work, we show that by learning representations through the use of deep convolutional neural networks (CNN), a significant increase in performance can be obtained on these tasks. The novel CNN approach used in this research is made to classify and achieve robust age group and gender classification of unconstrained images. This study has been evaluated and tested on both Essex face dataset and Adience benchmark for gender prediction and age estimation. The results obtained show that the proposed method provide a significant improvement in performance, our model obtains the state-of-the-art performance in both age and gender classification.
Highlights
Human face image analysis is an important research area in the field of pattern recognition and computer vision, in which many researchers concentrate on creating new or improving existing algorithms to several face perception tasks, including face recognition, age classification, gender recognition, etc
Human facial image processing research is undergoing in many directions and it still active and interesting, where age estimation and gender distinction from face images play important roles in many computer vision based applications as parental controls of the websites, video services and shopping recommendation systems [1]
Many methods have been proposed to tackle the age and gender classification task, the convolutional neural networks (CNN) is one of them more recently it has been employed in face image based age and gender classification tasks [2][3]
Summary
Human face image analysis is an important research area in the field of pattern recognition and computer vision, in which many researchers concentrate on creating new or improving existing algorithms to several face perception tasks, including face recognition, age classification, gender recognition, etc. Human facial image processing research is undergoing in many directions and it still active and interesting, where age estimation and gender distinction from face images play important roles in many computer vision based applications as parental controls of the websites, video services and shopping recommendation systems [1]. As face images vary in a wide range under the unconstrained conditions (namely, in the wild), the performances of CNN still need to be improved, especially in age estimation tasks [4]. We provide a significant improvement in performance and enhance the recognition accuracy of age and gender classification
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