Age Estimation Based on Multi-Region Convolutional Neural Network

Ting Liu, Jun Wan, Tingzhao Yu, Zhen Lei, Stan Z. Li

Abstract: As one of the most important biologic features, age has tremendous application potential in various areas such as surveillance, human-computer interface and video detection. In this paper, a new convolutional neural network, namely MRCNN (Multi-Region Convolutional Neural Network), is proposed based on multiple face subregions. It joins multiple face subregions together to estimation age. Each targeted region is analyzed to explore the contribution degree to age estimation. According to the face geometrical property, we select 8 subregions, and construct 8 sub-network structures respectively, and then fuse at feature-level. The proposed MRCNN has two principle advantages: 8 sub-networks are able to learn the unique age characteristics of the corresponding subregion and the eight networks are packaged together to complement age-related information. Further, we analyze the estimation accuracy on all age groups. Experiments on MORPH illustrate the superior performance of the proposed MRCNN.