Evaluation of Histograms Local Features and Dimensionality Reduction for 3D Face Verification

  • Received : 2015.07.08
  • Accepted : 2015.12.31
  • Published : 2016.09.30


The paper proposes a novel framework for 3D face verification using dimensionality reduction based on highly distinctive local features in the presence of illumination and expression variations. The histograms of efficient local descriptors are used to represent distinctively the facial images. For this purpose, different local descriptors are evaluated, Local Binary Patterns (LBP), Three-Patch Local Binary Patterns (TPLBP), Four-Patch Local Binary Patterns (FPLBP), Binarized Statistical Image Features (BSIF) and Local Phase Quantization (LPQ). Furthermore, experiments on the combinations of the four local descriptors at feature level using simply histograms concatenation are provided. The performance of the proposed approach is evaluated with different dimensionality reduction algorithms: Principal Component Analysis (PCA), Orthogonal Locality Preserving Projection (OLPP) and the combined PCA+EFM (Enhanced Fisher linear discriminate Model). Finally, multi-class Support Vector Machine (SVM) is used as a classifier to carry out the verification between imposters and customers. The proposed method has been tested on CASIA-3D face database and the experimental results show that our method achieves a high verification performance.


  1. Y. Ming, "Rigid-area orthogonal spectral regression for efficient 3D face recognition," Neurocomputing, vol. 129, pp. 445-457, 2014.
  2. Y. Ming, Q. Ruan, and X. Wang, "Efficient 3d face recognition with Gabor patched spectral regression," Computing and Informatics, vol. 31, no. 4, pp. 779-803, 2012.
  3. A. Mian, M. Bennamoun, and R. Owens, "Face recognition using 2D and 3D multimodal local features," in Advances in Visual Computing. Heidelberg: Springer, 2006, pp. 860-870.
  4. F. Hajati, A. A. Raie, and Y. Gao, "2.5 D face recognition using Patch Geodesic Moments," Pattern Recognition, vol. 45, no. 3, pp. 969-982, 2012.
  5. C. Xu, S. Li, T. Tan, and L. Quan, "Automatic 3D face recognition from depth and intensity Gabor features," Pattern Recognition, vol. 42, no. 9, pp. 1895-1905, 2009.
  6. T. Ahonen, A. Hadid, and M. Pietikainen, "Face recognition with local binary patterns," in Computer Vision-ECCV 2004. Heidelberg: Springer, 2004, pp. 469-481.
  7. C. H. Chan, M. A. Tahir, J. Kittler, and M. Pietikainen, "Multiscale local phase quantization for robust component-based face recognition using kernel fusion of multiple descriptors," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 5, pp. 1164-1177, 2013.
  8. C. H. Chan, J. Kittler, N. Poh, T. Ahonen, and M. Pietikainen, "(Multiscale) Local phase quantisation histogram discriminant analysis with score normalisation for robust face recognition," in Proceedings of IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), Kyoto, Japan, pp. 633-640.
  9. Z. Lei, T. Ahonen, M. Pietikainen, and S. Z. Li, "Local frequency descriptor for low-resolution face recognition," in Proceedings of IEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG 2011), Santa Barbara, CA, pp. 161-166.
  10. L. Wolf, T. Hassner, and Y. Taigman, "Descriptor based methods in the wild," in Workshop on Faces in 'Real-Life' Images: Detection, Alignment, and Recognition, Marseille, France, 2008.
  11. D. Huang, C. Shan, M. Ardabilian, Y. Wang, and L. Chen, "Local binary patterns and its application to facial image analysis: a survey," IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 41, no. 6, pp. 765-781, 2011.
  12. T. Ahonen, E. Rahtu, V. Ojansivu, and J. Heikkila, "Recognition of blurred faces using local phase quantization," in Proceedings of 19th International Conference on Pattern Recognition (ICPR 2008), Tampa, FL, 2008, pp. 1-4.
  13. M. Turk and A. P. Pentland, "Face recognition using eigenfaces," in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'91), Maui, HI, 1991, pp. 586-591.
  14. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, "Eigenfaces vs. fisherfaces: recognition using class specific linear projection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, 1997.
  15. M. Safayani and M. T. M. Shalmani, "Three-dimensional modular discriminant analysis (3DMDA): a new feature extraction approach for face recognition," Computers & Electrical Engineering, vol. 37, no. 5, pp. 811-823, 2011.
  16. C. Liu and H. Wechsler, "Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition," IEEE Transactions on Image processing, vol. 11, no. 4, pp. 467-476, 2002.
  17. X. He, D. Cai, S. Yan, and H. J. Zhang, "Neighborhood preserving embedding," in Proceedings of 10th IEEE International Conference on Computer Vision (ICCV2005), Beijing, China, 2005, pp. 1208-1213.
  18. X. He, S. Yan, Y. Hu, P. Niyogi, and H. J. Zhang, "Face recognition using Laplacianfaces," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 328-340, 2005.
  19. D. Cai, X. He, J. Han, and H. J. Zhang, "Orthogonal Laplacianfaces for face recognition," IEEE Transactions on Image Processing, vol. 15, no. 11, pp. 3608-3614, 2006.
  20. A. Chouchane, M. Belahcene, A. Ouamane, and S. Bourennane, "3D face recognition based on histograms of local descriptors," in Proceedings of 4th International Conference on Image Processing Theory, Tools and Applications (IPTA), Paris, France, 2014, pp. 1-5.
  21. A. Chouchane, M. Belahcene, A. Ouamane, and S. Bourennane, "Multimodal face recognition based on histograms of three local descriptors using score level fusion," in Proceedings of 2014 5th European Workshop on Visual Information Processing (EUVIP), Paris, France, 2014, pp. 1-6.
  22. P. Bagchi, D. Bhattacharjee, M. Nasipuri, and D. K. Basu, "A novel approach for nose tip detection using smoothing by weighted median filtering applied to 3D face images in variant poses," in Proceedings of 2012 International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME), Salem, India, 2012, pp. 272-277.
  23. X. Zhou, H. Seibert, C. Busch, and W. Funk, "A 3d face recognition algorithm using histogram-based features," in Proceedings of the 1st Eurographics conference on 3D Object Retrieval, Crete, Greece, 2008, pp. 65-71.
  24. H. D. Liu, M. Yang, Y. Gao, and C. Cui, "Local histogram specification for face recognition under varying lighting conditions," Image and Vision Computing, vol. 32, no. 5, pp. 335-347, 2014.
  25. L. Wang, R. Li, K. Wang, and C. Cao, "OLPP-based Gabor feature dimensionality reduction for facial expression recognition," in Proceedings of 2014 IEEE International Conference on Information and Automation (ICIA), Hailar, China, 2014, pp. 455-460.
  26. B. Yuan, H. Cao, and J. Chu, "Combining local binary pattern and local phase quantization for face recognition," in Proceedings of 2012 International Symposium on Biometrics and Security Technologies (ISBAST), Taipei, Taiwan, 2012, pp. 51-53.
  27. H. Soyel and H. Demirel, "Localized discriminative scale invariant feature transform based facial expression recognition," Computers & Electrical Engineering, vol. 38, no. 5, pp. 1299-1309, 2012.
  28. J. Kannala and E. Rahtu, "BSIF: binarized statistical image features," in Proceedings of 21st International Conference on Pattern Recognition (ICPR), Tsukuba, Japan, 2012, pp. 1363-1366.
  29. V. Ojansivu and J. Heikkila, "Blur insensitive texture classification using local phase quantization," in Image and Signal Processing. Heidelberg: Springer, 2008, pp. 236-243.
  30. A. Hadid, J. Ylioinas, and M. B. Lopez, "Face and texture analysis using local descriptors: a comparative analysis," in Proceedings of 2014 4th International Conference on Image Processing Theory, Tools and Applications (IPTA), Paris, France, 2014, pp. 1-4.
  31. T. Ojala, M. Pietikainen, T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Anal. vol. 24, no 7, pp. 971-987, 2002.
  32. L. Zhao, Y. Song, Y. Zhu, C. Zhang, and Y. Zheng, "Face recognition based on multi-class SVM," in Proceedings of Chinese Control and Decision Conference (CCDC'09), Guilin, China, 2009, pp. 5871-5873.
  33. M. Roschani, "Evaluation of local descriptors on the labeled faces in the wild dataset," Ph.D. dissertation, Institute for Anthropometrics, University of Karlsruhe, German, 2009.
  34. S. Meshgini, A. Aghagolzadeh, and H. Seyedarabi, "Face recognition using Gabor-based direct linear discriminant analysis and support vector machine," Computers & Electrical Engineering, vol. 39, no. 3, pp. 727-745, 2013.
  35. Y. Lei, M. Bennamoun, and A. A. El-Sallam, "An efficient 3D face recognition approach based on the fusion of novel local low-level features," Pattern Recognition, vol. 46, no. 1, pp. 24-37, 2013.
  36. C. W. Hsu and C. J. Lin, "A comparison of methods for multiclass support vector machines," IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 415-425, 2002.
  37. X. Wang, Q. Ruan, and Y. Ming, "3D face recognition using corresponding point direction measure and depth local features," in Proceedings of 2010 IEEE 10th International Conference on Signal Processing (ICSP), Beijing, China, pp. 86-89.
  38. Y. A. Li, Y. J. Shen, G. D. Zhang, T. Yuan, X. J. Xiao, and H. L. Xu, "An efficient 3D face recognition method using geometric features," in Proceedings of 2010 2nd International Workshop on Intelligent Systems and Applications (ISA), Wuhan, China, pp. 1-4.
  39. A. Ouamane, M., Belahcene, A. Benakcha, S. Bourennane, and A. Taleb-Ahmed, "Robust multimodal 2D and 3D face authentication using local feature fusion," Signal, Image and Video Processing, pp. 1-9, 2014. http://dx.doi/org/10.1007/s11760-014-0712-x.