3D Expression-Invariant Face Verification Based on Transfer Learning and Siamese Network for Small Sample Size

Electronics

Zhenye Li¹  Xinyan Sun¹  Hongyan Zou¹  Chao Ni¹  Tingting Zhu¹ 

[1] College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China;

关键词: face verification;    expression invariant;    transfer learning;    Siamese network;    face recognition;   

DOI  :  10.3390/electronics10172128

来源: DOAJ

【 摘 要 】

Three-dimensional (3D) face recognition has become a trending research direction in both industry and academia. However, traditional facial recognition methods carry high computational costs and face data storage costs. Deep learning has led to a significant improvement in the recognition rate, but small sample sizes represent a new problem. In this paper, we present an expression-invariant 3D face recognition method based on transfer learning and Siamese networks that can resolve the small sample size issue. First, a landmark detection method utilizing the shape index was employed for facial alignment. Then, a convolutional network (CNN) was constructed with transfer learning and trained with the aligned 3D facial data for face recognition, enabling the CNN to recognize faces regardless of facial expressions. Following that, the weighted trained CNN was shared by a Siamese network to build a 3D facial recognition model that can identify faces even with a small sample size. Our experimental results showed that the proposed method reached a recognition rate of 0.977 on the FRGC database, and the network can be used for facial recognition with a single sample.

【 授权许可】

Unknown