A comparative study on the importance of each face part in facial gender recognition via convolutional neural networks

Nowadays, gender recognition systems are very important in several fields such as security, human machine interaction, surveillance and targeted advertising. However, many factors, such as makeup and disguise, can affect recognition and extend the processing time. Our research revolves around this issue. This is a comparative experimental study of the significance of each part of the face (eyes, mouth, nose) in the gender facial recognition via convolutional neural networks (CNN). As a first step our goal is to find the most crucial part of the face in order to determine the most important part in the gender recognition. The used method was tested on the UTKFace dataset and the preliminary results confirm that the eyes contain the most discriminating information regarding gender identification. We achieve a classification accuracy of 92% for eyes, 91% for mouth and 89% for nose. Then we propose a second study on the degree of importance of the eyes for both genders by training the system using only eyes. We achieve a classification accuracy of 99% for eyes of men and 99% for eyes of women.