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2D Face Recognition

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Guide to Biometric Reference Systems and Performance Evaluation

Abstract

An overview of selected topics in face recognition is first presented in this chapter. The BioSecure 2D-face Benchmarking Framework is also described, composed of open-source software, publicly available databases and protocols. Three methods for 2D-face recognition, exploiting multiscale analysis, are presented. The first method exploits anisotropic smoothing, combined Gabor features and Linear Discriminant Analysis (LDA). The second approach is based on subject-specific face verification via Shape-Driven Gabor Jets (SDGJ), while the third combines Scale Invariant Feature Transform (SIFT) descriptors with graph matching.

Comparative results are reported within the benchmarking framework on the BANCA database (with Mc and P protocols). Experimental results on the FRGCv2 database are also reported. The results show the improvements achieved with the presented multiscale analysis methods in order to cope with mismatched enrollment and test conditions.

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Notes

  1. 1.

    Images created by the matlab code from P. D. Kovesi. MATLAB and Octave Functions for computer Vision and Image Processing. School of Computer Science & Software Engineering, The University of Western Australia. Available from: http://www.csse.uwa.edu.au/~pk/research/matlabfns/

References

  1. B. Achermann and H. Bunke. Combination of classifiers on the decision level for face recognition. Technical Report IAM-96-002, 1996.

    Google Scholar 

  2. G. Antonini, V. Popovici, and J. Thiran. Independent Component Analysis and Support Vector Machine for Face Feature Extraction. In 4th International Conference on Audio- and Video-Based Biometric Person Authentication, Guildford, UK, volume 2688 of Lecture Notes in Computer Science, pages 111–118, Berlin, 200IEEE.

    Google Scholar 

  3. Ognjen Arandjelovic and Roberto Cipolla. Face recognition from face motion manifolds using robust kernel resistor-average distance. In CVPRW '04: Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5, page 88, Washington, DC, USA, 200IEEE Computer Society.

    Google Scholar 

  4. Stefano Arca, Paola Campadelli, and Raffaella Lanzarotti. A face recognition system based on automatically determined facial fiducial points. Pattern Recognition, 39(3):432–443, 2006.

    Article  MATH  Google Scholar 

  5. O. Ayinde and Y.H. Yang. Face recognition approach based on rank correlation of Gaborfiltered images. Pattern Recognition, 35(6):1275–1289, June 2002.

    Article  MATH  Google Scholar 

  6. E. Bailly-Baillière, S. Bengio, F. Bimbot, M. Hamouz, J. Kittler, J. Mariéthoz, J. Matas, K. Messer, V. Popovici, F. Porée, B. Ruiz, and J.-P. Thiran. The BANCA Database and Evaluation Protocol. In 4th International Conference on Audio-and Video-Based Biometric Person Authentication (AVBPA'03), volume 2688 of Lecture Notes in Computer Science, pages 625–638, Guildford, UK, January 200Springer.

    Google Scholar 

  7. A. Bartlett and JR Movellan. Face recognition by independent component analysis. IEEE Trans. Neural Networks, 13:303–321, November 2002.

    Article  Google Scholar 

  8. M. Bartlett, G. Littlewort, I. Fasel, and J. Movellan. Real time face detection and facial expression recognition: Development and application to human-computer interaction. In Computer Vision and Pattern Recognition for Human-Computer Interaction, 2003.

    Google Scholar 

  9. Selin Baskan, M. Mete Bulut, and Volkan Atalay. Projection based method for segmentation of human face and its evaluation. Pattern Recognition Letters, 23(14):1623–1629, 2002.

    Article  MATH  Google Scholar 

  10. R. Basri and D. Jacobs. Lambertian reflectance and linear subspaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 383–390, 2003.

    Google Scholar 

  11. G. Baudat and F. Anouar. Generalized discriminant analysis using a kernel approach. Neural Comput., 12(10):2385–2404, 2000.

    Article  Google Scholar 

  12. S. Belongie, J. Malik, and J. Puzicha. Shape matching and object recognition using shape contexts. Transactions on Pattern Analysis and Machine Intelligence, 24(4):509–522, Apr 2002.

    Article  Google Scholar 

  13. Chris Boehnen and Trina Russ. A fast multi-modal approach to facial feature detection. In WACV-MOTION '05: Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05), volume 1, pages 135–142, Washington, DC, USA, 200IEEE Computer Society.

    Google Scholar 

  14. F.L. Bookstein. Principal warps: Thin-Plate Splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(6):567–585, 1989.

    Article  MATH  Google Scholar 

  15. K.I. Chang, K.W. Bowyer, and P.J. Flynn. Face recognition using 2D and 3D facial data. Workshop in Multimodal User Authentication, pages 25–32, 2003.

    Google Scholar 

  16. K.I. Chang, K.W. Bowyer, and P.J. Flynn. An evaluation of multimodal 2D+3D face biometrics. IEEE Trans. Pattern Anal. Mach. Intell., 27(4):619–624, 2005.

    Article  Google Scholar 

  17. Longbin Chen, Lei Zhang, Hongjiang Zhang, and Abdel-Mottaleb M. 3D shape constraint for facial feature localization using probabilistic-like output. Automatic Face and Gesture Recognition, 200Proceedings. Sixth IEEE International Conference on, pages 302–307, 17–19 May 2004.

    Google Scholar 

  18. Longbin Chen, Lei Zhang, Long Zhu, Mingjing Li, and Hongjiang Zhang. A novel facial feature point localization algorithm using probabilistic-like output. Proc. Asian Conference on Computer Vision (ACCV), 2004.

    Google Scholar 

  19. I. Cohen, N. Sebe, L. Chen, A. Garg, and T. Huang. Facial expression recognition from video sequences: Temporal and static modeling. in Computer Vision and Image Understanding, 91:160–187, 2003.

    Article  Google Scholar 

  20. D. Cristinacce, T. Cootes, and I. Scott. A multi-stage approach to facial feature detection. In 15th British Machine Vision Conference, London, England, pages 277–286, 2004.

    Google Scholar 

  21. J. Daugman. How iris recognition works. Circuits and Systems for Video Technology, IEEE Transactions on, 14(1):21–30, Jan. 2004.

    Article  Google Scholar 

  22. John G. Daugman. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. Journal of the Optical Society of America, 2(7):1160, 1985.

    Article  Google Scholar 

  23. O. Déniz, Modesto Castrillón Santana, Javier Lorenzo, and Mario Hernández. An incremental learning algorithm for face recognition. In ECCV '02: Proceedings of the International ECCV 2002 Workshop Copenhagen on Biometric Authentication, pages 1–9, London, UK, 200Springer-Verlag.

    Google Scholar 

  24. Kieron Messer et al. Face authentication test on the BANCA database. In ICPR '04: Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) volume 4, pages 523–532, Washington, DC, USA, 200IEEE Computer Society.

    Google Scholar 

  25. B. Fasel. Multiscale facial expression recognition using convolutional neural networks. in Proc. of the third Indian Conference on Computer Vision (ICVGIP), 2002.

    Google Scholar 

  26. I. Fasel and J. R. Movellan. Comparison of neurally inspired face detection algorithms. in Proc. of Int. Conf. on Artificial Neural Networks (ICANN), 2002.

    Google Scholar 

  27. R.S. Feris and V. Krüger. Hierarchical wavelet networks for facial feature localization. In FGR '02: Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, page 125, Washington, DC, USA, 200IEEE Computer Society.

    Google Scholar 

  28. R.A Fisher. The use of multiple measures in taxonomic problems. Ann. Eugenics, 7:179–188, 1936.

    Article  Google Scholar 

  29. Patrick J. Flynn. Biometrics databases. In A.K. Jain, A. Ross, and P. Flynn, editors, Handbook of Biometrics, pages 529–54Springer, 2008.

    Google Scholar 

  30. K. Fukui and O. Yamaguchi. Face recognition using multiview point patterns for robot vision. In Robotics Research The Eleventh International Symposium, pages 260–26Springer, 2003.

    Google Scholar 

  31. D. Gonzalez-Jimenez and J. L. Alba-Castro. Shape-driven Gabor jets for face description and authentication. Information Forensics and Security, IEEE Transactions on, 2(4):769–780, Dec. 2007.

    Article  Google Scholar 

  32. G. Gordon and M. Lewis. Face recognition using video clips and mug shots. Proceedings of the Office of National Drug Control Policy (ONDCP) International Technical Symposium (Nashua, NH), October 1995.

    Google Scholar 

  33. N. Gourier, D. Hall, and J.L. Crowley. Facial features detection robust to pose, illumination and identity. Systems, Man and Cybernetics, 2004 IEEE International Conference on, 1:617–622 vol.1, 10–13 Oct. 2004.

    Google Scholar 

  34. Yves Grandvalet and Stéaphane Canu. Adaptive scaling for feature selection in SVMs. Neural Information Processing Systems, 2002.

    Google Scholar 

  35. Ralph Gross. Face databases. In Stan Z. Li and Anil K. Jain, editors, Handbook of Face Recognition, pages 301–32Springer, 2005.

    Google Scholar 

  36. Ralph Gross and Vladimir Brajovic. An image preprocessing algorithm for illumination invariant face recognition. In 4th International Conference on Audio- and Video-Based Biometric Person Authentication (AVBPA). Springer, June 2003.

    Google Scholar 

  37. H. Gu, G. Su, and C. Du. Feature points extraction from faces. In Image and Vision Computing, pages 154–158, 2003.

    Google Scholar 

  38. H. Gunduz, A. Krim. Facial feature extraction using topological methods. Image Processing, 200ICIP 200Proceedings. 2003 International Conference on, 1:I–673–6 vol.1, 14–17 Sept. 2003.

    Google Scholar 

  39. Ziad M. Hafed and Martin D. Levine. Face recognition using the discrete cosine transform. Int. J. Comput. Vision, 43(3):167–188, 2001.

    Article  MATH  Google Scholar 

  40. B. Heisele, T. Serre, M. Pontil, T. Vetter, and T. Poggio. Categorization by learning and combining object parts. Advances In Neural Information Processing Systems, 2002.

    Google Scholar 

  41. R. Herpers, M. Michaelis, K. H. Lichtenauer, and G. Sommer. Edge and keypoint detection in facial regions. In FG '96: Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96), page 212, Washington, DC, USA, 199IEEE Computer Society.

    Google Scholar 

  42. A. J. Howell and H. Buxton. Towards unconstrained face recognition from image sequences. In FG '96: Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96), page 224, Washington, DC, USA, 199IEEE Computer Society.

    Google Scholar 

  43. K.S. Huang and M.M. Trivedi. Streaming face recognition using multicamera video arrays. Pattern Recognition, 200Proceedings. 16th International Conference on, 4:213–216 vol.4, 2002.

    Google Scholar 

  44. Xiaolei Huang, Song Zhang, Yang Wang, Dimitris Metaxas, and Dimitris Samaras. A hierarchical framework for high resolution facial expression tracking. In CVPRW '04: Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 1, page 22, Washington, DC, USA, 200IEEE Computer Society.

    Google Scholar 

  45. Buciu I., Kotropoulos C., and Pitas I. ICA and Gabor representation for facial expression recognition. Image Processing, 200ICIP 200Proceedings. 2003 International Conference on, 2:II–855–8 vol.3, 14–17 Sept. 2003.

    Google Scholar 

  46. IV2: Identification par I'Iris et le Visage via la Vidéo. http://iv2.ibisc.fr/PageWeb-IV2.html.

  47. Spiros Ioannou, George Caridakis, Kostas Karpouzis, and Stefanos Kollias. Robust feature detection for facial expression recognition. J. Image Video Process., 2007(2):5–5, 2007.

    Google Scholar 

  48. A.K. Jain and B. Chandrasekaran. Dimensionality and sample size considerations in pattern recognition practice. IEEE Trans. Pattern Anal. Mach. Intell., 2:835–855, 1987.

    Google Scholar 

  49. F. Jain, A.K. Farrokhnia. Unsupervised texture segmentation using Gabor filters. Systems, Man and Cybernetics, 199Conference Proceedings., IEEE International Conference on, pages 14–19, 4–7 Nov 1990.

    Google Scholar 

  50. G. A. Khuwaja. An adaptive combined classifier system for invariant face recognition. Digital Signal Processing, 12:2146, 2001.

    Google Scholar 

  51. M. Kirby and L. Sirovich. Application of the Karhunen-Loeve procedure for the characterization of human faces. IEEE Trans. Pattern Analysis and Machine Intelligence, 12:103–108, Jan 1990.

    Article  Google Scholar 

  52. D.R. Kisku, A. Rattani, E. Grosso, and M. Tistarelli. Face identification by SIFT-based complete graph topology. Automatic Identification Advanced Technologies, 2007 IEEE Workshop on, pages 63–68, 7–8 June 2007.

    Google Scholar 

  53. C. Kotropoulos, A. Tefas, and I. Pitas. Morphological elastic graph matching applied to frontal face authentication under optimal and real conditions. In ICMCS '99: Proceedings of the IEEE International Conference on Multimedia Computing and Systems Volume II, page 934, Washington, DC, USA, 199IEEE Computer Society.

    Google Scholar 

  54. C.L. Kotropoulos, A. Tefas, and I. Pitas. Frontal face authentication using discriminating grids with morphological feature vectors. Multimedia, IEEE Transactions on, 2(1):14–26, Mar 2000.

    Article  Google Scholar 

  55. Norbert Krüger. An algorithm for the learning of weights in discrimination functions using a priori constraints. IEEE Trans. Pattern Anal. Mach. Intell., 19(7):764–768, 1997.

    Article  Google Scholar 

  56. J. Lange, C. von den Malsburg, R. P. Wurtz, and W. Konen. Distortion invariant object recognition in the dynamic link architecture. Transactions on Computers, 42(3):300–311, Mar 1993.

    Article  Google Scholar 

  57. JianHuang Lai, Pong C. Yuen, WenSheng Chen, Shihong Lao, and Masato Kawade. Robust facial feature point detection under nonlinear illuminations. In RATFG-RTS '01: Proceedings of the IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (RATFG-RTS '01), page 168, Washington, DC, USA, 200IEEE Computer Society.

    Google Scholar 

  58. C.-J. Lee and S.-D. Wang. Fingerprint feature extraction using Gabor filters. Electronics Letters, 35(4):288–290, 18 Feb 1999.

    Article  Google Scholar 

  59. D. D. Lee and H. S. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755):788–791, October 1999.

    Article  Google Scholar 

  60. Kuang-Chih Lee, Jeffrey Ho, Ming-Hsuan Yang, and David Kriegman. Video-based face recognition using probabilistic appearance manifolds. Proc. IEEE CVPR, 01:313, 2003.

    Google Scholar 

  61. Y. Li, S. Gong, and H. Liddell. Video-based online face recognition using identity surfaces. Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, 200Proceedings. IEEE ICCV Workshop on, pages 40–46, 2001.

    Google Scholar 

  62. Yongmin Li, Shaogang Gong, and H. Liddell. Support vector regression and classification based multi-view face detection and recognition. Automatic Face and Gesture Recognition, 200Proceedings. Fourth IEEE International Conference on, pages 300–305, 2000.

    Google Scholar 

  63. Yongmin Li, Shaogang Gong, and H. Liddell. Modelling faces dynamically across views and over time. Computer Vision, 200ICCV 200Proceedings. Eighth IEEE International Conference on, 1:554–559 vol.1, 2001.

    Article  Google Scholar 

  64. Ying li Tian, Takeo Kanade, and Jeffrey F. Cohn. Evaluation of Gabor-wavelet-based facial action unit recognition in image sequences of increasing complexity. In FGR '02: Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, page 229, Washington, DC, USA, 200IEEE Computer Society.

    Google Scholar 

  65. C. Liu and H. Wechsler. Independent component analysis of Gabor features for face recognition. IEEE Trans. Neural Networks, 14(4):919–928, July 2003.

    Article  Google Scholar 

  66. Chengjun Liu. Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE Trans. Pattern Anal. Mach. Intell., 26(5):572–581, 2004.

    Article  Google Scholar 

  67. C.J. Liu. Capitalize on dimensionality increasing techniques for improving face recognition grand challenge performance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(5):725–737, May 2006.

    Article  Google Scholar 

  68. Xiaoming Liu and Tsuhan Cheng. Video-based face recognition using adaptive hidden markov models. Computer Vision and Pattern Recognition, 200Proceedings. 2003 IEEE Computer Society Conference on, 1:I-340-I-345 vol.1, 18-20 June 2003.

    MathSciNet  Google Scholar 

  69. A.M. Lopez, F. Lumbreras, J. Serrat, and J.J. Villanueva. Evaluation of methods for ridge and valley detection. Transactions on Pattern Analysis and Machine Intelligence, 21(4):327–335, Apr 1999.

    Article  Google Scholar 

  70. D.G. Lowe. Object recognition from local scale-invariant features. Computer Vision, 199The Proceedings of the Seventh IEEE International Conference on, 2:1150–1157 vol.2, 1999.

    Article  Google Scholar 

  71. J Lu and KN Plataniotis. face recognition using kernel direct discriminant analysis algorithms. IEEE Trans. on Neural Networks, pages 117–126, January 2003.

    Google Scholar 

  72. J. Lu, K.N. Plataniotis, and A.N. Venetsanopoulos. Face recognition using LDA-based algorithms. IEEE Transactions on Neural Networks, 14(1):195–200, Jan 2003.

    Article  Google Scholar 

  73. Simon M. Lucas and Tzu-Kuo Huang. Sequence recognition with scanning n-tuple ensembles. In ICPR '04: Proceedings of the Pattern Recognition, 17th International Conference on (ICPR '04) Volume 3, pages 410–413, Washington, DC, USA, 200IEEE Computer Society.

    Google Scholar 

  74. S.M. Lucas. Continuous n-tuple classifier and its application to real-time face recognition. IEE Proceedings - Vision, Image, and Signal Processing, 145(5):343–348, 1998.

    Article  Google Scholar 

  75. C. Padgett M. N. Dailey, W. C. Cottrell and R. Adolphs. EMPATH: a neural network that categorizes facial expressions. Journal of Cognitive Science, pages 1158–1173, 2002.

    Google Scholar 

  76. AM Martinez and AC Kak. PCA versus LDA. IEEE Trans. Pattern Analysis and Machine Intelligence, 23:228–233, 2001.

    Article  Google Scholar 

  77. K. Messer, J. Matas, J. Kittler, J. Luettin, and G. Maitre. XM2VTSDB: The extended M2VTS database. In Proc. Second International Conference on Audio- and Video-based Biometric Person Authentication (AVBPA), 1999.

    Google Scholar 

  78. S. Mika, G. Rätsch, J. Weston, B. Schölkopf, and K.R. Mullers. Fisher discriminant analysis with kernels. Neural Networks for Signal Processing IX, 199Proceedings of the 1999 IEEE Signal Processing Society Workshop, pages 41–48, Aug 1999.

    Google Scholar 

  79. S. Mika, G. Rätsch, J. Weston, B. Schölkopf, A. Smola, and K. Müller. Invariant feature extraction and classification in kernel spaces. Advances in Neural Information Processing Systems 12, pages 526–532, 2000.

    Google Scholar 

  80. K.-R. Muller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf. An introduction to kernel-based learning algorithms. IEEE Trans. on Neural Networks, 12(2):181–201, Mar 2001.

    Article  Google Scholar 

  81. BioSecure NoE. http://share.int-evry.fr/svnview-eph/.

  82. D. Petrovska-Delacrétaz, S. Lelandais, J. Colineau, L. Chen, B. Dorizzi, E. Krichen, M.A. Mellakh, A. Chaari, S. Guerfi, J. DHose, M. Ardabilian, and B. Ben Amor. The iv2 multimodal (2d, 3d, stereoscopic face, talking face and iris) biometric database, and the iv2 2007 evaluation campaign. In In the proceedings of the IEEE Second International Conference on Biometrics: Theory, Applications (BTAS), Washington DC USA, September 2008.

    Google Scholar 

  83. Jonathon Phillips and Patrick J Flynn. Overview of the face recognition grand challenge. Proc. IEEE CVPR, June 2005.

    Google Scholar 

  84. P. Jonathon Phillips, Patrick Groether, and Ross Micheals. Evaluation methods in face recognition. In Stan Z. Li and Anil K. Jain, editors, Handbook of Face Recognition, pages 329–34Springer, 2005.

    Google Scholar 

  85. P. Jonathon Phillips, W. Todd Scruggs, Alice J. O Toole, Patrick J. Flynn, Kevin W. Bowyer, Cathy L. Schott, and Matthew Sharpe. FRVT 2006 and ICE 2006 Large-Scale Results (NIS-TIR 7408), March 2007.

    Google Scholar 

  86. P.J. Phillips, P.J. Flynn, T. Scruggs, K.W. Bowyer, and W. Worek. Preliminary face recognition grand challenge results. In Proceedings 7th International Conference on Automatic Face and Gesture Recognition, pages 15–24, 2006.

    Google Scholar 

  87. Belhumeur PN, Hespanha JP, and Kriegman DJ. Eigenfaces vs fisherfaces: Recognition using class specific linear projection. Proc of the 4th European Conference on Computer Vision, pages 45–58, April 1996.

    Google Scholar 

  88. Laiyun Qing, Shiguang Shan, and Xilin Chen. Face relighting for face recognition under generic illumination. Acoustics, Speech, and Signal Processing, 200Proceedings. (ICASSP '04). IEEE International Conference on, 5:V–733–6 vol.5, 17–21 May 2004.

    Google Scholar 

  89. Laiyun Qing, Shiguang Shan, and Wen Gao. Face recognition under varying lighting based on derivates of log image. In SINOBIOMETRICS, pages 196–204, 2004.

    Google Scholar 

  90. K. R. Rao and P. Yip. Discrete cosine transform: algorithms, advantages, applications. Academic Press Professional, Inc., San Diego, CA, USA, 1990.

    MATH  Google Scholar 

  91. Sarunas J. Raudys and Anil K. Jain. Small sample size effects in statistical pattern recognition: Recommendations for practitioners. IEEE Trans. Pattern Anal. Mach. Intell., 13(3):252–264, 1991.

    Article  Google Scholar 

  92. B. Raytchev and H. Murase. Unsupervised face recognition from image sequences. Image Processing, 200Proceedings. 2001 International Conference on, 1:1042–1045 vol.1, 2001.

    Google Scholar 

  93. Bisser Raytchev and Hiroshi Murase. Unsupervised recognition of multi-view face sequences based on pairwise clustering with attraction and repulsion. Comput. Vis. Image Underst., 91(1–2):22–52, 2003.

    Article  Google Scholar 

  94. Yeon-Sik Ryu and Se-Young Oh. Automatic extraction of eye and mouth fields from a face image using eigenfeatures and ensemble networks. Applied Intelligence, 17(2):171–185, 2002.

    Article  MATH  Google Scholar 

  95. M. Sadeghi, J. Kittler, and K. Messer. Modelling and segmentation of lip area in face images. IEE Proceedings on Vision, Image and Signal Processing, 149(3):179–184, Jun 2002.

    Article  MathSciNet  Google Scholar 

  96. A.A. Salah, H. Cinar, L. Akarun, and B. Sankur. Robust facial landmarking for registration. Annals of Telecommunications, 62(1–2):1608–1633, 2007.

    Google Scholar 

  97. August-Wilhelm M. Scheer, Fabio Roli, and Josef Kittler. Multiple Classifier Systems: Third International Workshop, MCS 2002, Cagliari, Italy, June 24–26, 200Proceedings (Lecture Notes in Computer Science). Springer, August 2002.

    Google Scholar 

  98. B Schölkopf, A Smola, and KR Muller. Nonlinear component analysis as a kernel eigenvalue problem. Technical Report No 44, December 1996.

    Google Scholar 

  99. M. Schulze, K. Scheffler, and K.W. Omlin. Recognizing facial actions with support vector machines. in Proc. PRASA, pages 93–96, 2002.

    Google Scholar 

  100. Gregory Shakhnarovich, III John W. Fisher, and Trevor Darrell. Face recognition from long-term observations. In ECCV '02: Proceedings of the 7th European Conference on Computer Vision-Part III, pages 851–868, London, UK, 200Springer-Verlag.

    Google Scholar 

  101. T. Shakunaga, K. Ogawa, and S. Oki. Integration of eigentemplate and structure matching for automatic facial feature detection. In FG '98: Proceedings of the 3rd. International Conference on Face & Gesture Recognition, page 94, Washington, DC, USA, 199IEEE Computer Society.

    Google Scholar 

  102. L.L. Shen and L. Bai. Gabor feature based face recognition using kernel methods. In AFGR04, pages 170–175, 2004.

    Google Scholar 

  103. Frank Y. Shih and Chao-Fa Chuang. Automatic extraction of head and face boundaries and facial features. Inf. Sci. Inf. Comput. Sci., 158(1):117–130, 2004.

    Google Scholar 

  104. S. Singh, A. Gyaourova, G. Bebis, and I. Pavlidis. Infrared and visible image fusion for face recognition. in Proc. of Int. Society for Optical Engineering (SPIE), 2004.

    Google Scholar 

  105. F. Smeraldi and J. Bigun. Retinal vision applied to facial features detection and face authentication. Pattern Recogn. Lett., 23(4):463–475, 2002.

    Article  MATH  Google Scholar 

  106. Karin Sobottka and Ioannis Pitas. A fully automatic approach to facial feature detection and tracking. In AVBPA '97: Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication, pages 77–84, London, UK, 199Springer-Verlag.

    Google Scholar 

  107. Z. Sun, G. Bebis, and Miller R. Object detection using feature subset selection. Pattern Recognition, 37:2165–2176, 2004.

    Article  Google Scholar 

  108. Daniel L. Swets and John (Juyang) Weng. Using discriminant eigenfeatures for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(8):831–836, 1996.

    Article  Google Scholar 

  109. Anastasios Tefas, Constantine Kotropoulos, and Ioannis Pitas. Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(7):735–746, 2001.

    Article  Google Scholar 

  110. Anastasios Tefas, Constantine Kotropoulos, and Ioannis Pitas. Face verification using elastic graph matching based on morphological signal decomposition. Signal Processing, 82(6):833–851, 2002.

    Article  MATH  Google Scholar 

  111. M. Turk. A random walk through eigenspace. IEICE Transactions on Information and Systems (Special Issue on Machine Vision Applications), 84(12):1586–1595, 2001.

    Google Scholar 

  112. M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1):71–86, 1991.

    Article  Google Scholar 

  113. M. Vukadinovic, D. Pantic. Fully automatic facial feature point detection using Gabor feature based boosted classifiers. Systems, Man and Cybernetics, 2005 IEEE International Conference on, 2:1692–1698 Vol. 2, 10–12 Oct. 2005.

    Article  Google Scholar 

  114. Haitao Wang, Stan Z. Li, Yangsheng Wang, and Weiwei Zhang. Illumination modeling and normalization for face recognition. In AMFG '03: Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures, page 104, Washington, DC, USA, 200IEEE Computer Society.

    Google Scholar 

  115. Y. Weiss. Deriving intrinsic images from image sequences. Computer Vision, 200ICCV 200Proceedings. Eighth IEEE International Conference on, 2:68–75 vol. 2, 2001.

    Article  Google Scholar 

  116. Juyang Weng, C.H. Evans, and Wey-Shiuan Hwang. An incremental learning method for face recognition under continuous video stream. Automatic Face and Gesture Recognition, 200Proceedings. Fourth IEEE International Conference on, pages 251–256, 2000.

    Google Scholar 

  117. J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, and V. Vapnik. Feature selection for support vector machines. in Advances in Neural Information Processing Systems, 13, 2001.

    Google Scholar 

  118. L. Wiskott. Phantom faces for face analysis. In ICIP '97: Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3, page 308, Washington, DC, USA, 199IEEE Computer Society.

    Google Scholar 

  119. Laurenz Wiskott, Jean-Marc Fellous, Norbert Krüger, and Christoph von der Malsburg. Face recognition by elastic bunch graph matching. IEEE Trans. on Pattern Analysis and Machine Intelligence, 19(7):775–779, 1997.

    Article  Google Scholar 

  120. Laurenz Wiskott and Christoph von der Malsburg. Recognizing faces by dynamic link matching. In Axel Wismüller and Dominik R. Dersch, editors, Symposion über biologische Informationsverarbeitung und Neuronale Netze-SINN '95, volume 16, pages 63–68, München, 1996.

    Google Scholar 

  121. K.W. Wong, K.M. Lam, and W.C. Siu. An efficient algorithm for human face detection and facial feature extraction under different conditions. Pattern Recognition, 34(10):1993–2004, October 2001.

    Article  MATH  Google Scholar 

  122. Rolf P. Würtz. Object recognition robust under translations, deformations, and changes in background. IEEE Trans. Pattern Anal. Mach. Intell., 19(7):769–775, 1997.

    Article  Google Scholar 

  123. Zhong Xue, Stan Z. Li, and Eam Khwang Teoh. Bayesian shape model for facial feature extraction and recognition. Pattern Recognition, 36(12):2819–2833, 2003.

    Article  MATH  Google Scholar 

  124. O. Yamaguchi, K. Fukui, and K.-I. Maeda. Face recognition using temporal image sequence. Automatic Face and Gesture Recognition, 199Proceedings. Third IEEE International Conference on, pages 318–323, 1998.

    Google Scholar 

  125. Hua Yu and Jie Yang. A direct LDA algorithm for high-dimensional data – with application to face recognition. Pattern Recognition, 34(10):2067–2070, 2001.

    Article  MATH  Google Scholar 

  126. J. Zhang, Y. Yan, and M. Lades. Face recognition: Eigenface, elastic matching, and neural nets. PIEEE, 85(9):1423–1435, September 1997.

    Google Scholar 

  127. S. Zhang and S.-T. Yau. High-resolution, real-time 3D absolute coordinate measurement based on a phase-shifting method. Opt. Express 14, pages 2644–2649, 2006.

    Google Scholar 

  128. M. Zhou and H. Wei. Face verification using Gabor wavelets and AdaBoost. In ICPR, pages 404–407, 2006.

    Google Scholar 

  129. S.H. Zhou, V. Krueger, and R. Chellappa. Probabilistic recognition of human faces from video. CVIU, 91(1–2):214–245, July 2003.

    Google Scholar 

  130. S.K. Zhou and R. Chellappa. Probabilistic identity characterization for face recognition. Computer Vision and Pattern Recognition, 200CVPR 200Proceedings of the 2004 IEEE Computer Society Conference on, 2:II–805–812 Vol. 2, 27 June-2 July 2004.

    Google Scholar 

  131. X. Zhu, J. Fan, and A.K. Elmagarmid. Towards facial feature extraction and verification for omni-face detection in video/images. In Proc. ICIP, 2:II–113–II–116 vol. 2, 2002.

    Google Scholar 

  132. M. Zobel, A. Gebhard, D. Paulus, J. Denzler, and H. Niemann. Robust facial feature localization by coupled features. Proc. Fourth IEEE Int. Conf. Automatic Face and Gesture Recognition, pages 2–7, 2000.

    Google Scholar 

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Acknowledgments

We would like to thank the Italian Ministry of Research (PRIN framework project), a special grant from the Italian Ministry of Foreign Affairs under the India-Italy mutual agreement and to the European sixth framework program under the Network of Excellence BioSecure (IST-20026507604).

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Authors and Affiliations

  1. Universitá degli Studi di Sassari, Piazza Universitá 21, 07100, Sassari, Italy

    Massimo Tistarelli & Manuele Bicego

  2. TSC Department ETSE Telecomunicación, Campus Universitario de Vigo, 36310, Vigo, Spain

    José L. Alba-Castro

  3. TSC Department ETSE Telecomunicación, Campus Universitario de Vigo, 36310, Vigo, Spain

    Daniel Gonzàlez-Jiménez

  4. TELECOM SudParis (ex GET-INT), 91011, Evry, France

    Mohamed-Anouar Mellakh

  5. Computer Engineering Dept., Currently with 13 Centre of Mathematics and Computer Science (CWI), Formerly with Boğaziçi University, Kruislaan 413, 1090 GB, Amsterdam, The Netherlands

    Albert Ali Salah

  6. TELECOM SudParis (ex GET-INT), 9 rue Charles Fourier, 91011, Evry Cedex, France

    Dijana Petrovska-Delacrétaz

  7. TELECOM SudParis (ex GET-INT), 9 rue Charles Fourier, 91011, Evry Cedex, France

    Bernadette Dorizzi

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  1. Massimo Tistarelli
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  2. Manuele Bicego
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  4. Daniel Gonzàlez-Jiménez
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  5. Mohamed-Anouar Mellakh
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  6. Albert Ali Salah
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  7. Dijana Petrovska-Delacrétaz
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  8. Bernadette Dorizzi
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Correspondence to Massimo Tistarelli .

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Editors and Affiliations

  1. Electronics and Physics Department, TELECOM SudParis, France

    Dijana Petrovska-Delacrétaz PhD

  2. Centre National de la Recherche Scientifique (CNRS-LTCI), TELECOM ParisTech, France

    Bernadette Dorizzi (Professor) (Professor)

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Tistarelli, M. et al. (2009). 2D Face Recognition. In: Petrovska-Delacrétaz, D., Dorizzi, B., Chollet, G. (eds) Guide to Biometric Reference Systems and Performance Evaluation. Springer, London. https://doi.org/10.1007/978-1-84800-292-0_8

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