Age Estimation Based on Convolutional Neural Network

In recent years, face recognition technology has become a hot topic in the field of pattern recognition. The human face is one of the most important human biometric characteristics, which contains a lot of important information, such as identity, gender, age, expression, race and so on. Human age is a significant reference for identity discrimination, and age estimation can be potentially applied in human-computer interaction, computer vision and business intelligence. This paper addresses the problem of accurate estimation of human age. An age estimation system is generally composed of aging feature extraction and feature classification. In the feature extraction part, well-known texture descriptors like the Gabor wavelets and the Local Binary Patterns (LBP) have been utilized for the feature extraction. In our method, we use Convolutional Neural Network (CNN) to extract facial features. We gain the convolution activation features through building a multilevel CNN model based-on abundant training data. In the feature classification part, we divide different ages into 13 groups and use the Support Vector Machine (SVM) classifier to perform the classification. The experimental results show that the performance of the proposed method is superior to that of the previous methods when using our aging database.