Deep Residual Neural Network for Age Classification with Face Image

Abstract

One of the challenges in computer vision is age classification. There have been many methods used to classify someone age from the image of their faces. Convolutional neural network (CNN) gives a high accuracy but it cannot be used on many layers. Therefore, a residual technique is applied on convolutional neural network then named residual neural network. In this paper, some Residual Networks are applied to develop an age classification with face image using the Adience dataset that has 19,370 face images from 2,284 individuals grouped into eight categories: 0-2, 4-6, 8-13, 15-20, 25-32, 38-43, 48-53, and 60-100 years. Three techniques: cyclical learning rate, data augmentation, and transfer learning are observed. Six training scenarios are performed to select the best model. Experimental results show that Resnet34 is the best model with an average F1 score of 0.792 that is achieved by data augmentation, transfer learning, and trained on the image with size 224 x 224 pixels.