JACIII Vol.21 No.3 pp. 448-455
doi: 10.20965/jaciii.2017.p0448


Identity Verification Based on Facial Pose Pool and Bag of Words Model

Wangbin Chu and Yepeng Guan

School of Communication and Information Engineering, Shanghai University
99 Shangda Road, BaoShan District, Shanghai, China

Corresponding author

November 8, 2016
December 14, 2016
Online released:
May 19, 2017
May 20, 2017
identity verification, facial pose pool, incremental clustering, bag of words model, gaussian mixture model

There are many challenges for face based identity verification. It is one of fundamental topics in image processing and video analysis, and so on. A novel approach has been developed for facial identity verification based on a facial pose pool, which is constructed in an incremental clustering way to find both facial spatial information and orientation diversity. Bag of words is selected to extract image features from the facial pose pool in affine SIFT descriptor. The visual codebook is generated in k-means and Gaussian mixture model. Posterior pseudo probabilities are used to compute the similarities between each visual word and corresponding local features for image representation. Comparisons with some state-of-the-arts have highlighted the superior performance of the proposed method.

  1. [1] Z. Wang, Z. Miao, Q. Wu, Y. Wan, and Z. Tang, “Low-resolution face recognition: a review,” The Visual Computer, Vol.30, No.4, pp. 359-386, 2014.
  2. [2] S. Y. Liu and F. Kai, “Common and Adapted vocabularies for face verification,” IEICE Trans. on Information and Systems, Vol.98, No.12, pp. 2337-2340, 2015.
  3. [3] X. N. Hou, S. H. Ding, et al., “Similarity metric learning for face verification using sigmoid decision function,” The Visual Computer, Vol.32, No.4, pp. 479-490, 2016.
  4. [4] Y. Gao and H. J. Lee, “Cross-pose face recognition based on multiple virtual views and alignment error,” Pattern Recognition Letters, Vol.65, pp. 170-176, 2015.
  5. [5] M. Fischer, H. K. Ekenel, and R. Stiefelhagen, “Person re-identification in TV series using robust face recognition and user feedback,” Multimedia Tools and Applications, Vol.55, No.1, pp. 83-104, 2011.
  6. [6] K. Singh, M. Zaveri, and M. Raghuwanshi, “Rough set based pose invariant face recognition with mug shot images,” J. of Intelligent and Fuzzy Systems, Vol.26, No.2, pp. 523-539, 2014.
  7. [7] H. M. Takallou and S. Kasaei, “Head pose estimation and face recognition using a non-linear tensor-based model,” IET Computer Vision, Vol.8, No.1, pp. 54-65, 2014.
  8. [8] J. Lee, J. S. Park, G. J. Jang, and Y. H. Seo, “Efficient head pose determination and its application to face recognition on multi-pose face DB,” Int. J. of Multimedia and Ubiquitous Engineering, Vol.11, No.2, pp. 49-56, 2016.
  9. [9] W. Li and D. Chen, “Multi-pose face recognition combining tensor face and manifold learning,” Proc. of IEEE Int. Conf. on Computer Science and Automation Engineering, Vol.4, pp. 543-547, 2011.
  10. [10] Z. Ding and Y. Ma, “Manifold-based face gender recognition for video,” Proc. of Int. Conf. on Computer Science and Network Technology, Vol.2, pp. 1104-1107, 2011.
  11. [11] A. Dahmane, S. Larabi, I.M. Bilasco, and C. Djeraba, “Head pose estimation based on face symmetry analysis,” Signal, Image and Video Proc., Vol.9, No.8, pp. 1871-1880, 2015.
  12. [12] R. Zhu, G. Sang, Y. Cai, J. You, and Q. Zhao, “Head pose estimation with improved random regression forests,” Biometric Recognition, pp. 457-465, 2013.
  13. [13] Y. Cai, M. Yang, and J. Li, “Multiclass classification based on a deep convolutional network for head pose estimation,” Frontiers of Information Technology & Electronic Engineering, Vol.16, pp. 930-939, 2015.
  14. [14] B. Ma, W. Zhang, S. Shan, X. Chen, and W. Gao, “Robust head pose estimation using LGBP,” Proc. of Int. Conf. on Pattern Recognition, Vol.2, pp. 512-515, 2006.
  15. [15] Y. Liu, J. Chen, Z. Su, Z. Luo, N. Luo, L. Liu, and K. Zhang, “Robust head pose estimation using Dirichlet-tree distribution enhanced random forests,” Neurocomputing, Vol.173, pp. 42-53, 2016.
  16. [16] Z. F. Zhang and B. Yang, “Application of head pose estimation based on combination with particle filter algorithm and mean shift,” Int. J. of Simulation Systems, Science and Technology, Vol.16, No.1B, pp. 19.1-19.5, 2015.
  17. [17] J. Trefný and J. Matas, “Extended set of local binary patterns for rapid object detection,” Proc. of the Computer Vision Winter Workshop, pp. 1-7, 2010.
  18. [18] M. Krol, A. Florek, “Two-stage data reduction for a SVM classifier in a face recognition algorithm based on the active shape model,” Advances in Intelligent and Soft Computing, Vol.95, No.4, pp. 647-656, 2011.
  19. [19] J. Zhao, T. Xiong, Z. Yuan, L. Li, M. Yu, B. Chen, and M. Ji, “Fast AAM face recognition with combined Haar classifiers and skin color segmentation,” J. of Computational Information Systems, Vol.8, No.7, pp. 2799-2806, 2012.
  20. [20] S. O. Shahdi and S. A. R. Abu-Bakar, “Varying pose face recognition using combination of discrete cosine & wavelet transforms,” Proc. of Int. Conf. on Intelligent and Advanced Systems, Vol.2, pp. 642-647, 2012.
  21. [21] D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. of Computer Visionx, Vol.60, No.2, pp. 91-110, 2012.
  22. [22] G. Yu and J. M. Morel, “A fully affine invariant image comparison method,” Proc. of IEEE Int. Conf. on Acoustics, Speech and Signal Processing, pp. 1597-1600, 2009.
  23. [23] V. Blanz and T. Vetter, “Face recognition based on fitting a 3D morphable model,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.25, No.9, pp. 1063-1074, 2003.
  24. [24] P. Paysan, R. Knothe, B. Amberg, S. Romdhani, and T. Vetter,“A 3D face model for pose and illumination invariant face recognition,” Proc. of IEEE Int. Conf. on Advanced Video and Signal based Surveillance, pp. 296-301, 2009.
  25. [25] P. Huber, Z. H. Feng, W. Christmas, J. William, and M. Ratsch, “Fitting 3D morphable face models using local features,” Proc. of IEEE Int. Conf. on Image Processing, pp. 1195-1199, 2015.
  26. [26] O. M. Parkhi, A. Vedaldi, and A. Zisserman, “Deep face recognition,” Proc. of British Machine Vision Conf., Vol.1, p. 12, 2015.
  27. [27] A. Asthana et al., “Fully automatic pose-invariant face recognition via 3D pose normalization,” Proc. of IEEE Int. Conf. on Computer Vision, pp. 937-944, 2011.
  28. [28] A. Li, S. Shan, and W. Gao, “Coupled bias-variance tradeoff for cross-pose face recognition,” IEEE Trans. on Image Processing, Vol.21, No.1, pp. 305-315, 2012.
  29. [29] H. Gao, H. K. Ekenel, and R. Stiefelhagen, “Combining view-based pose normalization and feature transform for cross-pose face recognition,” Proc. of Int. Conf. on Biometrics, pp. 487-492, 2015.
  30. [30] S. J. D. Prince, J. H. Elder, J. Warrell, and F. M. Felisberti, “Tied factor analysis for face recognition across large pose differences,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.30, No.6, pp. 970-984, 2008.
  31. [31] G. S. Hsu, H. C. Peng, and K. H. Chang, “Landmark based facial component reconstruction for recognition across pose,” Proc. of IEEE Conf. on Computer Vision and Pattern Recognition Workshops, pp. 34-39, 2014.
  32. [32] M. S. Sarfraz and O. Hellwich, “Probabilistic learning for fully automatic face recognition across pose,” Image and Vision Computing, 28, No.5, pp. 744-753, 2010.
  33. [33] X. Zhang, D. S. Pham, S. Venkatesh, W. Liu, and D. Phung, “Mixed-norm sparse representation for multi view face recognition,” Pattern Recognition, Vol.48, No.9, pp. 2935-2946, 2015.
  34. [34] T. Deselaers, L. Pimenidis, and H. Ney, “Bag-of-visual-words models for adult image classification and filtering,” Proc. of Int. Conf. on Pattern Recognition, pp. 1-4, 2008.
  35. [35] Y. S. Wu, H. S. Liu, G. H. Ju, T. W. Lee, and Y. L. Chiu, “Using the visual words based on affine-SIFT descriptors for face recognition,” Proc. of IEEE Signal and Information Proc. Association Annual Summit and Conf., pp. 1-5, 2012.
  36. [36] W. Zhou, H. Li, Y. Lu, M. Wang, and Q. Tian, “Visual word expansion and BSIFT verification for large-scale image search,” Multimedia Systems, Vol.21, No.3, pp. 245-254, 2015.
  37. [37] R. Xu, et al., “Multiple human detection and tracking based on head detection for real-time video surveillance,” Multimedia Tools and Applications, Vol.74, No.3, pp. 729-742, 2015.
  38. [38] Y. Guan and Y. Huang, “Multi-pose human head detection and tracking boosted by efficient human head validation using ellipse detection,” Engineering Applications of Artificial Intelligence, Vol.37, pp. 181-193, 2015.
  39. [39] E. Boutellaa, et al., “Face verification using local binary patterns generic histogram adaptation and Chi-square based decision,” Proc. of European Workshop on Visual Information Processing, pp. 142-147, 2013.
  40. [40] J. Jacob and J. Anitha, “Analysis of symmetric local graph structure (SLGS) for face recognition with various thresholds,” Proc. of Int. Conf. on Electronics and Communication Systems, pp. 734-738, 2015.
  41. [41] J. J. Black, M. Gargesha, K. Kahol, P. Kuchi, and S. Panchanathan, “Framework for performance evaluation of face recognition algorithms,” Proc. of Convergence of Information Technologies and Communications, Int. Society for Optics and Photonics, pp. 163-174, 2002.
  42. [42] J. M. Morel and G. Yu, “ASIFT: a new framework for fully affine invariant image comparison,” J. on Imaging Sciences, Vol.2, No.2, pp. 438-469, 2009.
  43. [43] A. Agarwal and B. Triggs, “Multilevel image coding with hyperfeatures,” Int. J. of Computer Vision, Vol.78, No.1, pp. 15-27, 2008.
  44. [44] J. Sivic and A. Zisserman, “Efficient visual search of videos cast as text retrieval,” IEEE Trans. on Pattern Analysis and Machine Intelligencex, Vol.31, No.4, pp. 591-606, 2008.
  45. [45] G. Carneiro, A. B. Chan, P. J. Moreno, and N. Vasconcelos, “Supervised learning of semantic classes for image annotation and retrieval,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.29, No.3, pp. 394-410, 2007.
  46. [46] Y. Wan, X. Liu, J. Bing, and Y. Chen, “Online image classifier learning for Google image search improvement,” Proc. of IEEE Int. Conf. on Information and Automation, pp. 103-110, 2011.
  47. [47] T. Sim, S. Baker, and M. Bsat, “The CMU pose, illumination, and expression (PIE) database,” Proc. of Int. Conf. on Automatic Face and Gesture Recognition, pp. 46-51, 2002.
  48. [48] K. C. Lee, et al., “Visual tracking and recognition using probabilistic appearance manifolds,” Computer Vision and Image Understanding, Vol.99, No.3, pp. 303-331, 2005.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on May. 24, 2017