Advertisement

Local Representation of Facial Features

  • Joni-Kristian Kämäräinen
  • Abdenour Hadid
  • Matti Pietikäinen

Abstract

Feature extraction is one of the fundamental tasks in computer vision and image processing. Respectively, the task of selecting the best set of features to describe faces for recognition, verification, localization, or detection, is a fundamental problem in face biometrics. In this chapter, we review the most popular and successful features for face biometrics. In general, one should include complete algorithms when comparing the features, but certain extraction methods seem to maintain popularity due to their continuous success in various methods and approaches in biometrics and other fields of computer vision and image processing. This chapter specifically describes in more details two prominent local facial features, the first one based on Gabor filter responses, and the second on more recently proposed local binary patterns (LBPs).

Keywords

Face Recognition Face Image Local Binary Pattern Gabor Filter Gabor Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

Abdenour Hadid and Matti Pietikäinen thank the Academy of Finland for the financial support.

References

  1. 1.
    Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. In: Proc. of the ECCV (2004) Google Scholar
  2. 2.
    Ahonen, T., Hadid, A., Pietikäinen, M.: Face description with local binary patterns: Application to face recognition. IEEE Transactions on PAMI 28(12) (2006) Google Scholar
  3. 3.
    Ahonen, T., Pietikäinen, M.: Pixelwise local binary pattern models of faces using kernel density estimation. In: Proc. of the International Conference on Biometrics (2009) Google Scholar
  4. 4.
    Ahonen, T., Pietikäinen, M., Hadid, A., Mäenpää, T.: Face recognition based on the appearance of local regions. In: Proc. of the ICPR (2004) Google Scholar
  5. 5.
    Ahonen, T., Rahtu, E., Ojansivu, V., Heikkilä, J.: Recognition of blurred faces using local phase quantization. In: Proc. of the ICPR (2008) Google Scholar
  6. 6.
    Arca, S., Campadelli, P., Lanzarotti, R.: A face recognition system based on automatically termined fiducial points. Pattern Recognit. 39, 432–443 (2006) CrossRefMATHGoogle Scholar
  7. 7.
    Ashraf, A., Lucey, S., Chen, T.: Learning patch correspondences for improved viewpoint invariant face recognition. In: Proc. of the CVPR (2008) Google Scholar
  8. 8.
    Bastiaans, M.J.: Gabor’s signal expansion and the Zak transform. Appl. Opt. 33(23), 5241–5255 (1994) CrossRefGoogle Scholar
  9. 9.
    Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. PAMI 19(7) (1997) Google Scholar
  10. 10.
    Bicego, M., Lagorio, A., Grosso, E., Tistarelli, M.: On the use of SIFT features for face authentication. In: Proc. of the CVPR (2006) Google Scholar
  11. 11.
    Castrillón, M., Déniz, O., Hernández, D., Lorenzo, J.: A comparison of face and facial feature detectors based on the Viola–Jones general object detection framework. Mach. Vis. Appl. 22, 481–494 (2011). doi: 10.1007/s00138-010-0250-7 Google Scholar
  12. 12.
    Chan, C.-H., Kittler, J., Messer, K.: Multi-scale local binary pattern histograms for face recognition. In: Proc. of the International Conference on Biometrics (2007) Google Scholar
  13. 13.
    Chen, J., Shan, S., He, C., Zhao, G., Pietikäinen, M., Chen, X., Gao, W.: WLD: A robust local image descriptor. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1705–1720 (2010) CrossRefGoogle Scholar
  14. 14.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proc. of the CVPR (2005) Google Scholar
  15. 15.
    Daubechies, I.: The wavelet transform, time-frequency localization and signal analysis. IEEE Trans. Inf. Theory 36(5) (1990) Google Scholar
  16. 16.
    Daugman, J.G.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J. Opt. Soc. Am. A 2(7), 1160–1169 (1985) CrossRefGoogle Scholar
  17. 17.
    Daugman, J.G.: Complete discrete 2-D Gabor transform by neural networks for image analysis and compression. IEEE Trans. Acoust. Speech Signal Process. 36(7) (1988) Google Scholar
  18. 18.
    Daugman, J.: High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. PAMI 25(9) (1993) Google Scholar
  19. 19.
    Ding, L., Martinez, A.: Precise detailed detection of faces and facial features. In: Proc. of the CVPR (2008) Google Scholar
  20. 20.
    Ekenel, H., Stiefelhagen, R.: Analysis of local appearance-based face recognition: Effects on feature selection and feature normalization. In: Proc. of the CVPR (2006) Google Scholar
  21. 21.
    Etemad, K., Chellappa, R.: Discriminant analysis for recognition of human face images. J. Opt. Soc. Am. A 14, 1724–1733 (1997) CrossRefGoogle Scholar
  22. 22.
    Feichtinger, H., Strohmer, T. (eds.): Gabor Analysis and Algorithms. Birkhäuser, Basel (1998) MATHGoogle Scholar
  23. 23.
    Feng, X., Pietikäinen, M., Hadid, A.: Facial expression recognition with local binary patterns and linear programming. Pattern Recognit. Image Anal. 15(2), 546–548 (2005) Google Scholar
  24. 24.
    Figueiredo, M., Jain, A.: Unsupervised learning of finite mixture models. IEEE Trans. PAMI 24(3) (2002) Google Scholar
  25. 25.
    Gabor, D.: Theory of communication. J. Inst. Electr. Eng. 93, 429–457 (1946) Google Scholar
  26. 26.
    GMMBayes Toolbox for Matlab. http://www2.it.lut.fi/project/gmmbayes
  27. 27.
    Gökberk, B., Irfanoglu, M., Akarun, L., Alpaydin, E.: Learning the best subset of local features for face recognition. Pattern Recognit. 40, 1520–1532 (2007) CrossRefMATHGoogle Scholar
  28. 28.
    Granlund, G.H.: In search of a general picture processing operator. Comput. Graph. Image Process. 8, 155–173 (1978) CrossRefGoogle Scholar
  29. 29.
    Hadid, A., Pietikäinen, M.: Combining appearance and motion for face and gender recognition from videos. Pattern Recognit. 42(11), 2818–2827 (2009) CrossRefGoogle Scholar
  30. 30.
    Hadid, A., Pietikäinen, M., Ahonen, T.: A discriminative feature space for detecting and recognizing faces. In: Proc. of the CVPR (2004) Google Scholar
  31. 31.
    Hadid, A., Zhao, G., Ahonen, T., Pietikäinen, M.: Face analysis using local binary patterns. In: Mirmehdi, M., Xie, X., Suri, J. (eds.) Handbook of Texture Analysis, pp. 347–373. Imperial College Press, London (2008) CrossRefGoogle Scholar
  32. 32.
    Hamouz, M., Kittler, J., Kamarainen, J.-K., Paalanen, P., Kalviainen, H., Matas, J.: Feature-based affine-invariant localization of faces. IEEE Trans. PAMI 27(9) (2005) Google Scholar
  33. 33.
    Hjelmas, E., Low, B.K.: Face detection: A survey. Comput. Vis. Image Underst. 83(3), 236–274 (2001) CrossRefMATHGoogle Scholar
  34. 34.
    Hua, G., Akbarzadeh, A.: A robust elastic and partial matching metric for face recognition. In: Proc. of the ICCV (2009) Google Scholar
  35. 35.
    Huang, X., Li, S.Z., Wang, Y.: Shape localization based on statistical method using extended local binary pattern. In: Proc. of the International Conference on Image and Graphics (2004) Google Scholar
  36. 36.
    Huang, X., Li, S., Wang, Y.: Jensen–Shannon boosting learning for object recognition. In: Proc. of the CVPR (2005) Google Scholar
  37. 37.
    Ilonen, J., Kamarainen, J.-K., Paalanen, P., Hamouz, M., Kittler, J., Kälviäinen, H.: Image feature localization by multiple hypothesis testing of Gabor features. IEEE Trans. Image Process. 17(3) (2008) Google Scholar
  38. 38.
    Jain, A., Chen, Y., Demirkus, M.: Pores and ridges: Fingerprint matching using level 3 features. IEEE Trans. PAMI 29(1) (2007) Google Scholar
  39. 39.
    Kamarainen, J.-K., Kyrki, V., Kälviäinen, H.: Invariance properties of Gabor filter based features—overview and applications. IEEE Trans. Image Process. 15(5), 1088–1099 (2006) CrossRefGoogle Scholar
  40. 40.
    Kamarainen, J.-K., Hamouz, M., Kittler, J., Paalanen, P., Ilonen, J., Drobchenko, A.: Object localisation using generative probability model for spatial constellation and local image features. In: Proc. of the ICCV Workshop on Non-Rigid Registration and Tracking Through Learning (2007) Google Scholar
  41. 41.
    Kanade, T.: Picture processing system by computer complex and recognition of human faces. Doctoral dissertation, Kyoto University (1973) Google Scholar
  42. 42.
    Kozakaya, T., Shibata, T., Yuasa, M., Yamaguchi, O.: Facial feature localization using weighted vector concentration approach. Image Vis. Comput. 28, 772–780 (2010) CrossRefGoogle Scholar
  43. 43.
    Kyrki, V., Kamarainen, J.-K., Kälviäinen, H.: Simple Gabor feature space for invariant object recognition. Pattern Recognit. Lett. 25(3), 311–318 (2003) CrossRefGoogle Scholar
  44. 44.
    Lades, M., Vorbrüggen, J.C., Buhmann, J., Lange, J., von der Malsburg, C., Würtz, R.P., Konen, W.: Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. Comput. 42, 300–311 (1993) CrossRefGoogle Scholar
  45. 45.
  46. 46.
    Lee, P.-H., Hsu, G.-S., Hung, Y.-P.: Face verification and identification using facial trait code. In: Proc. of the CVPR (2009) Google Scholar
  47. 47.
    Li, S.Z., Jain, A.K. (eds.): Handbook of Face Recognition. Springer, New York (2005) MATHGoogle Scholar
  48. 48.
    Li, S., Zhao, C., Zhu, X., Lei, Z.: Learning to fuse 3D+2D based face recognition at both feature and decision levels. In: Proc. of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures (2005) Google Scholar
  49. 49.
    Li, S.Z., Chu, R., Liao, S., Zhang, L.: Illumination invariant face recognition using near-infrared images. IEEE Trans. PAMI 29(4) (2007) Google Scholar
  50. 50.
    Liang, L., Xiao, R., Wen, F., Sun, J.: Face alignment via component-based discriminative search. In: Proc. of the ECCV (2008) Google Scholar
  51. 51.
    Liao, S., Zhu, X., Lei, Z., Zhang, L., Li, S.Z.: Learning multi-scale block local binary patterns for face recognition. In: Proc. of the International Conference on Biometrics (2007) Google Scholar
  52. 52.
    Liao, S., Law, M., Chung, A.: Dominant local binary patterns for texture classification. IEEE Trans. Image Process. 18(5), 1107–1118 (2009) CrossRefGoogle Scholar
  53. 53.
    Liu, C.-C., Dai, D.-Q.: Face recognition using dual-tree complex wavelet features. IEEE Trans. Image Process. 2593–2599 (2009) Google Scholar
  54. 54.
    Ma, B., Zhang, W., Shan, S., Chen, X., Gao, W.: Robust head pose estimation using LGBP. In: Proc. of the ICPR (2006) Google Scholar
  55. 55.
    Manjunath, B., Ohm, J.R., Vinod, V.V., Yamada, A.: Color and texture descriptors. IEEE Trans. Circuits Syst. Video Technol. 11(6) (2001). Special Issue on MPEG-7 Google Scholar
  56. 56.
    McCool, C., Marcel, S.: Parts-based face verification using local frequency bands. In: Proc. of the International Conference on Biometrics (2009) Google Scholar
  57. 57.
    Messer, K. et al.: Face authentication test on the BANCA database. In: Proc. of the ICPR (2004) Google Scholar
  58. 58.
    Messer, K., Matas, J., Kittler, J., Luettin, J., Maitre, G.: XM2VTSDB: The extended M2VTS database. In: Proc. of the International Conference on Audio and Video-based Biometric Person Authentication (1999) Google Scholar
  59. 59.
    Meyers, E., Wolf, L.: Using biologically inspired features for face processing. Int. J. Comput. Vis. 76, 93–104 (2008) CrossRefGoogle Scholar
  60. 60.
    Meynet, J., Popovici, V., Thiran, J.-P.: Face detection with boosted Gaussian features. Pattern Recognit. 40, 2283–2291 (2007) CrossRefMATHGoogle Scholar
  61. 61.
    Mian, A., Bennamoun, M., Owens, R.: Keypoint detection and local feature matching for textured 3d face recognition. Int. J. Comput. Vis. 79, 1–12 (2008) CrossRefGoogle Scholar
  62. 62.
    Moghaddam, B., Nastar, C., Pentland, A.: A Bayesian similarity measure for direct image matching. In: Proc. of the ICPR (1996) Google Scholar
  63. 63.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognit. 29, 51–59 (1996) CrossRefGoogle Scholar
  64. 64.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. PAMI 24 (2002) Google Scholar
  65. 65.
    Ojansivu, V., Heikkilä, J.: Blur insensitive texture classification using local phase quantization. In: Proc. of the International Conference on Image and Signal Processing (2008) Google Scholar
  66. 66.
    Paalanen, P., Kamarainen, J.-K., Ilonen, J., Kälviäinen, H.: Feature representation and discrimination based on Gaussian mixture model probability densities—practices and algorithms. Pattern Recognit. 39(7), 1346–1358 (2006) CrossRefMATHGoogle Scholar
  67. 67.
    Phillips, P., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. PAMI 22 (2000) Google Scholar
  68. 68.
    Pinto, N., DiCarlo, J., Cox, D.: How far can you get with a modern face recognition test set using only simple features? In: Proc. of the CVPR (2009) Google Scholar
  69. 69.
    Raja, Y., Gong, S.: Sparse multiscale local binary patterns. In: Proc. of the British Machine Vision Conference (2006) Google Scholar
  70. 70.
    Rodriguez, Y., Marcel, S.: Face authentication using adapted local binary pattern histograms. In: Proc. of the ECCV (2006) Google Scholar
  71. 71.
    Roy, A., Marcel, S.: Haar local binary pattern feature for fast illumination invariant face detection. In: Proc. of the British Machine Vision Conference (2009) Google Scholar
  72. 72.
    Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., Poggio, T.: Object recognition with cortex-like mechanisms. IEEE Trans. PAMI 29(3) (2007) Google Scholar
  73. 73.
    Shan, S., Zhang, W., Su, Y., Chen, X., Gao, W.: Ensemble of piecewise FDA based on spatial histograms of local (Gabor) binary patterns for face recognition. In: Proc. of the ICPR (2006) Google Scholar
  74. 74.
    Shan, C., Gong, S., McOwan, P.: Facial expression recognition based on local binary patterns:a comprehensive study. Image Vis. Comput. 27(6), 803–816 (2009) CrossRefGoogle Scholar
  75. 75.
    Shastri, B., Levine, M.: Face recognition using localized features based on non-negative sparse coding. Mach. Vis. Appl. 18, 107–122 (2007) CrossRefGoogle Scholar
  76. 76.
    SimpleGabor Toolbox for Matlab. http://www2.it.lut.fi/project/simplegabor
  77. 77.
    Su, Y., Shan, S., Chen, X., Gao, W.: Hierarchical ensemble of global and local classifiers for face recognition. IEEE Trans. Image Process. 18(8) (2009) Google Scholar
  78. 78.
    Sun, N., Zheng, W., Sun, C., Zou, C., Zhao, L.: Gender classification based on boosting local binary pattern. In: Proc. of the International Symposium on Neural Networks (2006) Google Scholar
  79. 79.
    Sun, Z., Tan, T., Qiu, X.: Graph matching iris image blocks with local binary pattern. In: Proc. of the International Conference on Biometrics (2006) Google Scholar
  80. 80.
    Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. In: ICCV Workshop on Analysis and Modeling of Faces and Gestures (2007) Google Scholar
  81. 81.
    Tan, X., Triggs, B.: Fusing Gabor and LBP feature sets for kernel-based face recognition. In: Proc. of the ICCV Workshop on Analysis and Modeling of Faces and Gestures (2007) Google Scholar
  82. 82.
    Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3, 71–86 (1991) CrossRefGoogle Scholar
  83. 83.
    Varma, M., Zisserman, A.: Texture classification: Are filter banks necessary? In: Proc. of the CVPR (2003) Google Scholar
  84. 84.
    Verbeek, J.J., Vlassis, N., Kröse, B.: Efficient greedy learning of Gaussian mixture models. Neural Comput. 5(2), 469–485 (2003) CrossRefGoogle Scholar
  85. 85.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Computer Vision and Pattern Recognition, pp. 511–518 (2001) Google Scholar
  86. 86.
    Wang, J., Yau, W., Wang, H.: Age categorization via ECOC with fused Gabor and LBP features. In: Proc. of the IEEE Workshop on Applications of Computer Vision (2009) Google Scholar
  87. 87.
    Wang, P., Ji, Q.: Multi-view face and eye detection using discriminant features. Comput. Vis. Image Underst. 105, 99–111 (2007) CrossRefGoogle Scholar
  88. 88.
    Winder, S., Brown, M.: Learning local image descriptors. In: Proc. of the CVPR (2007) Google Scholar
  89. 89.
    Wiskott, L., Fellous, J.-M., Krüger, N., von der Malsburg, C.: Face recognition by elastic bunch graph matching. IEEE Trans. PAMI 19 (1997) Google Scholar
  90. 90.
    Xu, Z., Chen, H., Zhu, S.-C., Luo, J.: A hierarchical compositional model for face representation and sketching. IEEE Trans. PAMI 30(6) (2008) Google Scholar
  91. 91.
    Yan, S., Shan, S., Chen, X., Gao, W.: Locally assembled binary (LAB) feature with feature-centric cascade for fast and accurate face detection. In: Proc. of the CVPR (2008) Google Scholar
  92. 92.
    Yang, J., Zhang, D., Frangi, A.F., yu Yang, J.: Two-dimensional PCA: A new approach to appearance-based face representation and recognition. IEEE Trans. PAMI 26 (2004) Google Scholar
  93. 93.
    Zhang, X., Jia, Y.: Face recognition with local steerable phase feature. Pattern Recognit. Lett. 27, 1927–1933 (2006) MathSciNetCrossRefGoogle Scholar
  94. 94.
    Zhang, G., Wang, Y.: Faceprint: Fusion of local features for 3d face recognition. In: Proc. of the International Conference on Biometrics (2009) Google Scholar
  95. 95.
    Zhang, G., Huang, X., Li, S.Z., Wang, Y., Wu, X.: Boosting local binary pattern LBP-based face recognition. In: Proc. of the Chinese Conference on Biometric Recognition (2004) Google Scholar
  96. 96.
    Zhang, W., Shan, S., Gao, W., Chen, X., Zhang, H.: Local Gabor binary pattern histogram sequence (LGBPHS): A novel non-statistical model for face representation and recognition. In: Proc. of the ICCV (2005) Google Scholar
  97. 97.
    Zhang, B., Shan, S., Chen, X., Gao, W.: Histogram of Gabor phase patterns (HGPP): A novel object representation approach for face recognition. IEEE Trans. Image Process. 16(1) (2007) Google Scholar
  98. 98.
    Zhang, B., Gao, Y., Zhao, S., Liu, J.: Local derivate pattern versus local binary pattern: Face recognition with high-order local pattern descriptor. Image Vis. Comput. 28, 772–780 (2010) CrossRefGoogle Scholar
  99. 99.
    Zhao, G., Pietikäinen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. PAMI 29(6) (2007) Google Scholar
  100. 100.
    Zhao, G., Pietikäinen, M.: Boosted multi-resolution spatiotemporal descriptors for facial expression recognition. Pattern Recognit. Lett. 30, 1117–1127 (2009) CrossRefGoogle Scholar
  101. 101.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Comput. Surv. 34(4), 399–458 (2003) CrossRefGoogle Scholar
  102. 102.
    Zhao, G., Barnard, M., Pietikäinen, M.: Lipreading with local spatiotemporal descriptors. IEEE Trans. Multimed. 11(7) (2009) Google Scholar
  103. 103.
    Zou, J., Ji, Q., Nagy, G.: A comparative study of local matching approach for face recognition. IEEE Trans. Image Process. 16(10) (2007) Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Joni-Kristian Kämäräinen
    • 1
  • Abdenour Hadid
    • 2
  • Matti Pietikäinen
    • 2
  1. 1.Machine Vision and Pattern Recognition LaboratoryLappeenranta University of TechnologyLappeenrantaFinland
  2. 2.Machine Vision Group, Dept. of Electrical and Information EngineeringUniversity of OuluOuluFinland

Personalised recommendations