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Face Recognition by 3D Registration for the Visually Impaired Using a RGB-D Sensor
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  • 作者:Wei Li (16)
    Xudong Li (17)
    Martin Goldberg (18)
    Zhigang Zhu (16) (18)

    16. The City College of New York
    ; New York ; NY ; 10031 ; USA
    17. Beihang University
    ; Beijing ; 100191 ; China
    18. The CUNY Graduate Center
    ; New York ; NY ; 10016 ; USA
  • 关键词:Face recognition ; Assistive computer vision ; 3D registration ; RGB ; D sensor
  • 刊名:Lecture Notes in Computer Science
  • 出版年:2015
  • 出版时间:2015
  • 年:2015
  • 卷:8927
  • 期:1
  • 页码:763-777
  • 全文大小:440 KB
  • 参考文献:1. Asthana, A., Marks, T.K., Jones, M.J., Tieu, K.H., Rohith, M.: Fully automatic pose-invariant face recognition via 3D pose normalization. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 937鈥?44. IEEE (2011)
    2. Berretti, S, Bimbo, A, Pala, P (2010) 3D face recognition using isogeodesic stripes. IEEE Transactions on Pattern Analysis and Machine Intelligence 32: pp. 2162-2177 CrossRef
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    4. Cao, X, Wei, Y, Wen, F, Sun, J (2014) Face alignment by explicit shape regression. International Journal of Computer Vision 107: pp. 177-190 CrossRef
    5. Huang, D., Zhang, G., Ardabilian, M., Wang, Y., Chen, L.: 3D face recognition using distinctiveness enhanced facial representations and local feature hybrid matching. In: 2010 Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), pp. 1鈥?. IEEE (2010)
    6. Jafri, R, Arabnia, HR (2009) A survey of face recognition techniques. JIPS 5: pp. 41-68
    7. Li, B.Y., Mian, A.S., Liu, W., Krishna, A.: Using kinect for face recognition under varying poses, expressions, illumination and disguise. In: 2013 IEEE Workshop on Applications of Computer Vision (WACV), pp. 186鈥?92. IEEE (2013)
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    10. Passalis, G, Perakis, P, Theoharis, T, Kakadiaris, IA (2011) Using facial symmetry to handle pose variations in real-world 3D face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 33: pp. 1938-1951 CrossRef
    11. Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the face recognition grand challenge. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 947鈥?54. IEEE (2005)
    12. Queirolo, C.C., Silva, L., Bellon, O.R., Pamplona Segundo, M.: 3D face recognition using simulated annealing and the surface interpenetration measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(2), 206鈥?19 (2010)
    13. Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: Deepface: Closing the gap to human level performance in face verification. In: IEEE CVPR (2014)
    14. Torr, PH, Zisserman, A (2000) Mlesac: A new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding 78: pp. 138-156 CrossRef
    15. Wang, Y, Liu, J, Tang, X (2010) Robust 3D face recognition by local shape difference boosting. IEEE Transactions on Pattern Analysis and Machine Intelligence 32: pp. 1858-1870 CrossRef
    16. Yang, H., Wang, Y.: A lbp-based face recognition method with hamming distance constraint. In: Fourth International Conference on Image and Graphics, ICIG 2007, pp. 645鈥?49. IEEE (2007)
    17. Zhang, Z (2012) Microsoft kinect sensor and its effect. IEEE MultiMedia 19: pp. 4-10 CrossRef
    18. Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2879鈥?886. IEEE (2012)
  • 作者单位:Computer Vision - ECCV 2014 Workshops
  • 丛书名:978-3-319-16198-3
  • 刊物类别:Computer Science
  • 刊物主题:Artificial Intelligence and Robotics
    Computer Communication Networks
    Software Engineering
    Data Encryption
    Database Management
    Computation by Abstract Devices
    Algorithm Analysis and Problem Complexity
  • 出版者:Springer Berlin / Heidelberg
  • ISSN:1611-3349
文摘
To help visually impaired people recognize people in their daily life, a 3D face feature registration approach is proposed with a RGB-D sensor. Compared to 2D face recognition methods, 3D data based approaches are more robust to the influence of face orientations and illumination changes. Different from most 3D data based methods, we employ a one-step ICP registration approach that is much less time consuming. The error tolerance of the 3D registration approach is analyzed with various error levels in 3D measurements. The method is tested with a Kinect sensor, by analyzing both the angular and distance errors to recognition performance. A number of other potential benefits in using 3D face data are also discussed, such as RGB image rectification, multiple-view face integration, and facial expression modeling, all useful for social interactions of visually impaired people with others.

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