Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (2): 595-600.DOI: 10.11772/j.issn.1001-9081.2021122214

• Multimedia computing and computer simulation • Previous Articles    

Deep face verification under pose interference

Qi WANG(), Hang LEI, Xupeng WANG   

  1. School of Information and Software Engineering,University of Electronic Science and Technology of China,Chengdu Sichuan 610054,China
  • Received:2022-01-04 Revised:2022-03-02 Accepted:2022-03-21 Online:2022-05-16 Published:2023-02-10
  • Contact: Qi WANG
  • About author:LEI Hang, born in 1960, Ph. D., professor. His research interests include embedded operating system.
    WANG Xupeng, born in 1986, Ph. D,research assistant. His research interests include computer vision, pattern recognition.
  • Supported by:
    National Natural Science Foundation of China(61802052)


王奇(), 雷航, 王旭鹏   

  1. 电子科技大学 信息与软件工程学院,成都 610054
  • 通讯作者: 王奇
  • 作者简介:雷航(1960—),男,四川自贡人,教授,博士,CCF会员,主要研究方向:嵌入式操作系统
  • 基金资助:


Face verification is widely used in various scenes in life, and the acquisition of ordinary RGB images is extremely dependent on illumination conditions. In order to solve the interference of illumination and head pose, a convolutional neural network based Siamese network L2-Siamese was proposed. Firstly, the paired depth images were taken as input. Then, after using two convolutional neural networks that share weights to extract facial features respectively, L2 norm was introduced to constrain the facial features with different poses on a hypersphere with a fixed radius. Finally, the fully connected layer was used to map the difference between the features to the probability value in (0,1) to determine whether the group of images belonged to the same object. In order to verify the effectiveness of L2-Siamese, a test was conducted on the public dataset Pandora. Experimental results show that L2-Siamese has good overall performance. After the dataset was grouped according to the size of head pose interference, the test results show that the prediction accuracy of L2-Siamese is 4 percentage points higher than that of the state-of-the-art algorithm fully-convolutional Siamese network under the maximum head pose interference, illustrating that the accuracy of prediction has been significantly improved.

Key words: face verification, depth image, deep learning, Siamese network, neural network



关键词: 人脸验证, 深度图, 深度学习, 孪生网络, 神经网络

CLC Number: