KSII Transactions on Internet and Information Systems (TIIS)

  • 제11권2호
  • /
  • Pages.785-804
  • /
  • 2017
  • /
  • 1976-7277(pISSN)
  • /
  • 1976-7277(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
권호 표지
DOI QR코드

DOI QR Code

Misclassified Samples based Hierarchical Cascaded Classifier for Video Face Recognition

  • Fan, Zheyi (School of Information and Electronics, Beijing Institute of Technology) ;
  • Weng, Shuqin (School of Information and Electronics, Beijing Institute of Technology) ;
  • Zeng, Yajun (School of Information and Electronics, Beijing Institute of Technology) ;
  • Jiang, Jiao (School of Information and Electronics, Beijing Institute of Technology) ;
  • Pang, Fengqian (School of Information and Electronics, Beijing Institute of Technology) ;
  • Liu, Zhiwen (School of Information and Electronics, Beijing Institute of Technology)
  • 투고 : 2016.07.02
  • 심사 : 2016.10.25
  • 발행 : 2017.02.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Due to various factors such as postures, facial expressions and illuminations, face recognition by videos often suffer from poor recognition accuracy and generalization ability, since the within-class scatter might even be higher than the between-class one. Herein we address this problem by proposing a hierarchical cascaded classifier for video face recognition, which is a multi-layer algorithm and accounts for the misclassified samples plus their similar samples. Specifically, it can be decomposed into single classifier construction and multi-layer classifier design stages. In single classifier construction stage, classifier is created by clustering and the number of classes is computed by analyzing distance tree. In multi-layer classifier design stage, the next layer is created for the misclassified samples and similar ones, then cascaded to a hierarchical classifier. The experiments on the database collected by ourselves show that the recognition accuracy of the proposed classifier outperforms the compared recognition algorithms, such as neural network and sparse representation.

키워드

참고문헌

  1. H. S. Bhatt, R. Singh and M. Vatsa, "On rank aggregation for face recognition from videos," in Proc. of 20th IEEE Int. Conf. on Image Processing, pp. 2993-2997, Sep. 15-18, 2013.
  2. W. Xu, S. Lee and E. Lee, "A Robust Method for Partially Occluded Face Recognition," KSII Transactions on Internet and Information Systems, vol. 9, no. 7, pp. 2667-2682, July, 2015. https://doi.org/10.3837/tiis.2015.07.019
  3. E. G. Ortiz, A. Wright and M. Shah, "Face recognition in movie trailers via mean sequence sparse representation-based classification," in 26th IEEE Conf. on Computer Vision and Pattern Recognition, pp. 3531-3538, June 23-28, 2013.
  4. Z. W. Huang, R. P. Wang, S. G. Shan and X. L. Chen, "Projection Metric Learning on Grassmann Manifold with Application to Video based Face Recognition," in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 140-149, June 7-12, 2015.
  5. R. G. Cinbis, J. Verbeek and C. Schmid, "Unsupervised metric learning for face identification in TV video," in Proc. of IEEE Int. Conf. on Computer Vision, pp. 1599-1566, Nov. 6-13, 2011.
  6. Y. C. Chen, V. M. Patel, P. J. Phillips and R. Chellappa, "Dictionary-based face recognition from video," in Proc. of IEEE European Conf. on Computer Vision, pp. 766-779, Oct. 7-13, 2012.
  7. H. Zheng, Q. Ye and Z. Jin, "A Novel Multiple Kernel Sparse Representation based Classification for Face Recognition," KSII Transactions on Internet and Information Systems, vol. 8, no. 4, pp. 1463-1480, Apr., 2014. https://doi.org/10.3837/tiis.2014.04.017
  8. L. Wolf and N. Levy, "The SVM-minus similarity score for video face recognition," in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 3523-3530, June 23-28, 2013.
  9. M. Kim, S. Kumar, V. Pavlovi and H. Rowley, "Face tracking and recognition with visual constraints in real-world videos," in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1-8, June 23-28, 2008.
  10. P. Liang, G. Teodoro, H. Ling, E. Blasch, G. Chen and L. Bai, "Multiple kernel learning for vehicle detection in wide area motion imagery," in 15th IEEE Int. Conf. on Information Fusion (FUSION), pp. 1629-1636, July 9-12, 2012.
  11. H. Wang and Y. Cai, "Monocular based road vehicle detection with feature fusion and cascaded Adaboost algorithm," Optik-International Journal for Light and Electron Optics, vol. 126, no. 22, pp. 3329-3334, Nov. 2015. https://doi.org/10.1016/j.ijleo.2015.08.018
  12. X. O. Tang and Z. F. Li, "Video based face recognition using multiple classifiers," in Proc. of the 6th IEEE Int. Conf. on Automatic Face and Gesture Recognition. pp. 345-349, May 17-19, 2004.
  13. J. F. Connolly, E. Granger and R. Sabourin, "An adaptive ensemble of fuzzy ARTMAP Neural Networks for video-based face classification," in IEEE Congress on Evolutionary Computation, pp. 1-8, July 18-23, 2010.
  14. N. Hassanpour and L. Chen, "A hierarchical training and identification method using Gaussian process models for face recognition in videos," in 11th IEEE Int. Conf. and Workshops on Automatic Face and Gesture Recognition, pp. 1-8, May 4-8, 2015.
  15. Y. Yang, "Face recognition algorithm based on cascade sparse representation-based classifier," Industry & Mine Automation, vol. 40, no. 5, pp. 46-48, May. 2014.
  16. Z. C. Li, J. Liu, J. H. Tang, and H. Q. Lu, "Robust Structured Subspace Learning for Data Representation," IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 37, no. 10, pp. 2085-2098, Oct. 2015. https://doi.org/10.1109/TPAMI.2015.2400461
  17. J. H. Tang, Z. C. Li, M. Wang and R. Z. Zhao, "Neighborhood discriminant hashing for large-scale image retrieval," IEEE Transactions on Image Processing, vol. 24, no. 9, pp. 2827-2840, Sep. 2015. https://doi.org/10.1109/TIP.2015.2421443
  18. Z. C. Li, J. Liu, Y. Yang, X. F. Zhou and H. Q. Lu, "Clustering-Guided Sparse Structural Learning for Unsupervised Feature Selection," IEEE Transactions on Knowledge & Data Engineering, vol. 26, no. 9, pp. 2138-2150, Sep. 2014. https://doi.org/10.1109/TKDE.2013.65
  19. Y. Freund and R. E. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," Journal of Computer and System and Science, vol. 55, no. 1, pp. 119-139, Jan. 1997. https://doi.org/10.1006/jcss.1997.1504
  20. G. Fumera, F. Roli and A. Serrau, "A theoretical analysis of Bagging as a linear combination of classifiers," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 7, pp. 1293-1299, July 2008. https://doi.org/10.1109/TPAMI.2008.30
  21. G. Fumera, F. Roli and A. Serrau, "Eigenfaces for recognition," Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, Jan. 1991. https://doi.org/10.1162/jocn.1991.3.1.71
  22. P. J. Phillips, H. Moon, S. A. Rizvi and P. J. Rauss, "The FERET evaluation methodology for face-recognition algorithms," IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 22, no. 10, pp. 1090-1104, Oct. 2000. https://doi.org/10.1109/34.879790
  23. A. S. Georghiades, P. N. Belhumeur and D. J. Kriegman, "From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose," IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 23, no. 6, pp. 643-660, June 2001. https://doi.org/10.1109/34.927464
  24. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry and Y. Ma, "Robust Face Recognition via Sparse Representation," IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 31, no. 2, pp. 210-227, Feb. 2009. https://doi.org/10.1109/TPAMI.2008.79
  25. S. Xuan, S. Xiang and H. Ma, "Subclass representation-based face-recognition algorithm derived from the structure scatter of training samples," IET Computer Vision, vol. 10, no. 6, pp. 493-502, Apr. 2016. https://doi.org/10.1049/iet-cvi.2015.0350
  26. L. Zhang,M. Yang and X. Feng, "Sparse representation or collaborative representation: Which helps face recognition?" in IEEE International Conference on Computer Vision, pp. 471-478, Nov. 6-13, 2011.
  27. P. Zhu, L. Zhang, Q. Hu and S. Shiu, "Multi-scale patch based collaborative representation for face recognition with margin distribution optimization," in European Conference on Computer Vision, pp. 822-835, Oct. 7-13, 2012.