CASIA OpenIR  > 毕业生  > 博士学位论文
Thesis Advisor刘昌平
Degree Grantor中国科学院大学
Place of Conferral北京
Keyword人脸活体检测 人脸防伪 展示攻击检测 生物识别安全
Other Abstract
With the rapid development and wide commercial applications of face recognition techniques, there are more and more security challenges made by spoofing faces. Due to advanced electronic devices and three-d printing technology, spoofing faces could be easily made, copied or exchanged in terms of high-definition photos, high-definition videos or face masks, which contributes to the difficulty and importance of effectual and efficient face anti-spoofing algorithms. To make face spoofing systems robuster and more effective, we adopt more advanced sensors apart from common cameras, and make use of dual cameras, the active near-infrared light, RGB-D images and multispectral images to detect spoofing faces. The main contributions of this work are listed as follows:
1. Face spoofing detection based on dual cameras: most spoofing faces behave rather differently in terms of depth information from genuine ones. With the help of dual cameras and stereo reconstruction techniques, we propose a spoofing detection method. On one hand, the texture differences caused by dual camera positions are explored; on the other hand, depth features are extracted based on the reconstructed three-d sparse point cloud. Two spoofing cues are fused to overcome their own deficiencies and to enhance the final classification results.
2. Face spoofing detection based on active near-infrared light: every face spoofing attack needs  certain kinds of spoofing media as displaying carriers, and we propose a spoofing detection method by analyzing the existence of spoofing media. By turning on and off the additional near-infrared light, we obtain the near-infrared differential image caused by the active light, and the context consistency between face and non-face areas is analyzed. At the same time, we further extract lighting texture cues based on the reflectance model, to boost the performance of cropped spoofing faces. 
3. Face spoofing detection based on RGB-D images: while using three-d information might significantly benefit face anti-spoofing systems, it is restrained by several issues such as the expensive hardware, high time cost or poor accessibility. Thus, we utilize RGB-D images captured by relatively low cost sensors, and propose a spoofing detection method. We analyze the correlation between color and depth images, and calculate the consistency among different parts of depth images. Finally two spoofing cues are fused in decision level to make proposed method robuster towards different spoofing media and enhance its generalization.
4. Face spoofing detection based on multispectral correlation analyses: motivated by the thought that it is easier for spoofing faces to cheat on one spectrum than defraud two different spectra, we propose a face spoofing detection system analyzing potential multispectral information between visual and near-infrared images. To handle different lighting conditions, we choose part-based schema and divide both images into smaller regions. A confidence map is also calculated during the  training process to measure the contribution of each patch, which increases the final classification accuracy and the generalization ability.
Document Type学位论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
孙旭东. 人脸活体检测中的关键问题研究[D]. 北京. 中国科学院大学,2018.
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人脸活体检测中的关键问题研究 - 孙旭东(5933KB)学位论文 暂不开放CC BY-NC-SAApplication Full Text
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