SUN Ziqiang, ZHU Yuesheng, LIU Xiyao, “A Novel 3D Video Fingerprinting Algorithm Based on Local Feature Points,” Chinese Journal of Electronics, vol. 27, no. 6, pp. 1192-1199, 2018, doi: 10.1049/cje.2018.08.010
Citation: SUN Ziqiang, ZHU Yuesheng, LIU Xiyao, “A Novel 3D Video Fingerprinting Algorithm Based on Local Feature Points,” Chinese Journal of Electronics, vol. 27, no. 6, pp. 1192-1199, 2018, doi: 10.1049/cje.2018.08.010

A Novel 3D Video Fingerprinting Algorithm Based on Local Feature Points

doi: 10.1049/cje.2018.08.010
Funds:  This work is supported by the Shenzhen Key Lab of Information Theory & Future Network Arch (No.ZDSYS201603311739428) and the Shenzhen Engineering Laboratory of Broadband Wireless Network Security.
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  • Corresponding author: ZHU Yuesheng (corresponding author) received the B.E. degree in radio engineering, M.E. degree in circuits and systems and Ph.D. degree in electronics engineering in 1982, 1989 and 1996, respectively. He is currently working as a professor at the Lab of Communication and Information Security, Shenzhen Graduate School, Peking University. He is a senior member of IEEE, fellow of China Institute of Electronics, and senior member of China Institute of Communications. His interests include digital signal processing, multimedia technology, communication and information security. (
  • Received Date: 2016-02-24
  • Rev Recd Date: 2016-09-02
  • Publish Date: 2018-11-10
  • Digital rights management of the 3D contents is a crucial open issue in the 3D video industry. A novel robust fingerprinting algorithm is proposed for protecting the copyright of the 3D video. Unlike the existing algorithms extracting visual features separately from the 2D videos and the depth maps, in our algorithm a novel local stereo space is constructed according to the depth information of the pixels around the extracted local feature points in the 2D videos, and the 3D videos are processed in a holistic manner. In the proposed space, the 3D-transform-feature is extracted and aggregated into a feature matrix, and then the compact 3D video fingerprints are obtained from the eigenspace of the matrix. Our comprehensive experiments are conducted on a 3D video database, and the results have demonstrated the robustness and discrimination of the proposed algorithm. Moreover, our fingerprints cost less storage spaces than the existing approaches.
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