Title: Feature similarity measurement of cross-age face images based on a deep learning algorithm

Authors: Dong-yuan Ge; Xi-fan Yao; Wen-jiang Xiang; En-chen Liu; Zhi-bin Xu

Addresses: School of Mechanical and Transportation Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China ' School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China ' School of Mechanical and Energy Engineering, Shaoyang University, Shaoyang 422004, China ' School of Mechanical and Transportation Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China ' School of Mechanical and Transportation Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China

Abstract: In order to overcome the problem of low accuracy of traditional facial image feature similarity measurement methods, the paper proposes a new cross-age facial image feature similarity measurement method based on a deep learning algorithm. Face segmentation is performed based on eye coordinates to determine the effective area for face detection. This study introduces deep learning algorithms to build the basic architecture of deep learning networks, define network input as face image data containing age and identity tags in a cross-age database, and define output as face image features under constant age, and pass the foundation separately training and cross-age training complete the network training, and calculate the facial features under the same age. The cosine distance method is used to measure the similarity of face image features across ages. The experimental results show that the method has obtained more accurate measurement results in different age data sets, and the highest measurement accuracy is 97.9%.

Keywords: deep learning algorithm; face image; similarity measurement; cosine distance method.

DOI: 10.1504/IJBM.2021.114653

International Journal of Biometrics, 2021 Vol.13 No.2/3, pp.272 - 288

Received: 19 May 2020
Accepted: 25 Jul 2020
Published online: 29 Apr 2021 *

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