[1]SUN Yan,LI Xujun,HE Qihong.Research on age invariant face verification technology[J].CAAI Transactions on Intelligent Systems,2021,16(2):247-253.[doi:10.11992/tis.202011029]
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Research on age invariant face verification technology

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 2 Page number: 247-253 Column: 学术论文—机器学习 Public date: 2021-03-05
Title:
Research on age invariant face verification technology
Author(s):
SUN Yan;  LI Xujun;  HE Qihong
School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China
Keywords:
face verification; deep learning; age interference; dual-coded average local binary pattern; histogram of oriented gradient; canonical correlation analysis
CLC:
TP391.41
DOI:
10.11992/tis.202011029
Abstract:
The texture, shape, and other features of the face will change dramatically with age, significantly reducing the accuracy of face recognition. To solve this problem, this study proposes a multitask cross-age face verification algorithm based on the dual coding average local binary pattern (DCALBP) and deep learning algorithm. First, the multitask convolutional neural network is used to detect the face area. Second, the DCALBP and histogram of oriented gradients are used to extract the face texture and shape features, respectively. Then, the canonical correlation analysis is conducted to merge the face texture and shape features to determine the facial age features. Finally, the Siamese network is employed to extract the facial features and separate the facial age features from the facial features, suppress the influence of age factors on face verification, and obtain age-invariant facial features. The algorithm can identify whether it is the same person by feature matching. In this study, the accuracy of the FGNet, MORPH Album2, and processed synthesis data sets is 89.73%, 98.32%, and 98.27%, respectively, and the effectiveness of the proposed method is fully verified.
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