Abstract
Paper introduces a 3-D shape representation scheme for automatic face analysis and identification, and demonstrates its invariance to facial expression. The core of this scheme lies on the combination of statistical shape modelling and non-rigid deformation matching. While the former matches 3-D faces with facial expression, the latter provides a low-dimensional feature vector that controls the deformation of model for matching the shape of new input, thereby enabling robust identification of 3-D faces. The proposed scheme is also able to handle the pose variation without large part of missing data. To assist the establishment of dense point correspondences, a modified free-form-deformation based on B-spline warping is applied with the help of extracted landmarks. The hybrid iterative closest point method is introduced for matching the models and new data. The feasibility and effectiveness of the proposed method was investigated using standard publicly available Gavab and BU-3DFE datasets, which contain faces with expression and pose changes. The performance of the system was compared with that of nine benchmark approaches. The experimental results demonstrate that the proposed scheme provides a competitive solution for face recognition.
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References
Chellappa R, Sinha P, Phillips PJ (2010) Face recognition by computers and humans. Comput IEEE Comput Soc 43(2):46–55
Chellppa R, Wilson C, Sirohey C (1995) Human and machine recognition of faces: a survey. Proc IEEE 83(5):705–740
Jafri R, Arabnia HR (2009) A survery of face recognition techniques. J Inf Process Syst 53(2):41–66
Kong SG, Heo J, Abidi BR, Paik J, Abidi M (2005) Recent advances in visual and infrared face recognition—a review. Comput Vis Image Underst 97:103–135
Samal A, Iyengar P (1992) Automatic recognition and analysis of human faces and facial expressions: a survey. Pattern Recognit 25:65–77
Lu Y, Zhou J, Yu S (2003) A survey of face detection. Extract Recognit Comput Inform Pattern Recognit 22:163–195
Hadid A, Pietikainen M, Ahonen T (2004) A discriminative feature space for detecting and recognising faces. In: IEEE conference on computer vision and pattern recognition, pp 797–804
Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86
Heseltine T, Pears N, Austin J, Chen Z (2003) Face recognition: a comparison of appearance-based approaches. Digital Image Comput Tech Appl 1:59–68
Delac K, Grgic M, Liatsis P (2005) Appearance-based statistical methods for face recognition. In: 47th international symposium ELMAR-2005 focused on multimedia systems and applications, pp 151–158
Hesher C, Srivastava A, Erlebacher G (2003) A novel technique for face recognition using range imaging. In: International symposium on signal processing and its applications, pp 201–204
Belhumeur P, Hespanha J, Kriegma D (1997) Eigenfaces vs. fisheraces: recognition using class specific projection. IEEE Trans Pattern Anal Mach Intell 19(7):328–340
Ahonen T, Hadid A, Pietikainen M (2004) Face recognition with local binary patterns. In: European conference on computer vision, pp 469–481
Bronstein A, Bronstein M, Kimmel R (2005) Three-dimensional face recognition. Int J Comput Vis 64(1):5–30
Mpiperis I, Malassiotis S, Strintzis M (2007) 3-D face recognition with the geodesic polar representation. IEEE Trans Inform Forensic Secur 2(3):537–547
Li X, Jia T, Zhang H (2009) Expression-insensitive 3D face recognition using sparse representation. In: Conference on computer vision and pattern recognition, pp 2575–2582
Huang D, Ardabilian M, Wang Y, Chen L (2012) 3-D face recognition using elbp-based facial description and local feature hybrid matching. IEEE Trans Inf Forensic Secur 7(5):1551–1565
Mohammadzade H, Hatzinakos D (2013) Iterative closest normal point for 3D face recognition. IEEE Trans Pattern Anal Mach Intell 35(2):381–397
Shi J, Samal A, Marx D (2006) Effective are landmarks and their geometry for face recognition. Comput Vis Image Underst 102(2006):117–133
Gupta S, Aggatwal JK, Markey MK, Bovik AC (2007) 3D face recognition founded on the structural diversity of human faces. In: IEEE international conference on computer vision and pattern recognition
Zhang L, Razdan A, Farin G, Femiani J, Bae M, Lockwood C (2006) 3D face authentication and recognition based on bilateral symmetry analysis. Vis Comput Comput Vis Image Underst 22(1)
Nagamine T, Uemura T, Masuda I (1992) 3-D facial image analysis for human identification. In: International conference on computer vision and pattern recognition
Samir C, Srivastava A, Acheroy M (2006) Three-dimensional face recognition using shapes of facial curves. IEEE Trans Pattern Anal Mach Intell 28(11):1858–1863
Drira H, Amor BB, Mohamed D, Srivastava A (2010) Pose and expression-invariant 3-D face recognition using elastic radial curves. In: British machine vision conference
Queirolo CC, Silva L, Bellon ORP, Segundo MP (2010) 3D face recognition using simulated annealing and the surface interpenetration measure. IEEE Trans Pattern Anal Mach Intell 32(2):206–219
Moreno AB, Sanchez A, Velez JF, Diaz FJ (2003) Face recognition using 3D surface-extracted descriptors. In: Conference on Irish machine vision and image processing
Hajati F, Raie AA, Goa Y (2012) 2.5D face recognition using patch geodesic moments. Pattern Recognit 45(2012):969–982
Gordan GG (1992) Face recognition based on depth and curvature features. In: International conference on computer vision and pattern recognition, pp 808–810
Tanaka HT, Ikeda M, Chiaki H (1998) Curvature-based face surface recognition using spherical correlation—principal directions for curved object recognition. In: International conference on automatic face and gesture recognition, pp 372–377
Wang Y, Liu J, Tang X (2010) Robust 3D face recognition by local shape difference boosting. IEEE Trans Pattern Anal Mach Intell 32(10):1858–1870
Srivastava A, Liu X, Hesher C (2006) Face recognition using optimal linear components of face images. J Image Vis Comput 24(3):291–299
Lu X, Jain AK (2008) Deformation modelling for robust 3D face matching. In: International conference on computer vision and pattern recognition
Haar FB, Veltkamp RC (2010) Expression modeling for expression-invariant face recognition. J Comput Graph 34(2010):231–241
Besl P, McKay N (1997) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256
Zhang Z (1994) Iterative point matching for registration of free-form curve and surfaces. Int J Comput Vis 13(1):119–152
He X, Yan S, Hu Y, Niyogi P, Zhang HJ (2005) Face recognition using Laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340
Belkin M, Niyogi P (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Conference on advances in neural information processing system
Jahne HB, HauBecker F (2000) Computer vision and application. Academic Press, London
Papiez BW, Matuszewski BJ, Shark L-K, Quan W (2012) Facial expression recognition using LOG-euclidean statistical shape models. In: International conference on pattern recognition applications and methods, ICPRAM
Papiez BW, Matuszewski BJ, Shark L-K, Quan W (2012) Facial expression recognition using diffeomorphic image registration framework. Math Methodol Pattern Recognit Mach Learn 30:179–194
Rohr K, Stiehl HS, Sprengel R, Buzug TM, Weese J, Kuhn MH (2001) Landmark-based elastic registration using approximating thin-plate splines. IEEE Trans Med Imaging 20(6):526–534
Joshi A (2009) Optimization of landmark selection for cortical surface registration. In: International conferences on computer vision and pattern recognition, pp 669–706
Lu X, Jain AK, Colbry D (2006) Matching 2.5D face scans to 3D models. IEEE Trans Pattern Anal Mach Intell 28(1):31–43
Quan W, Matuszewski BJ, Shark L-K (2012) Facial asymmetry analysis based on 3-D dynamic scans. In: IEEE international conference on system, man and cybernetics, pp 2676–2681
Kimmel R, Sethian JA (1998) Computing geodesic on manifolds. US Natl Acad Sci 95:8431–8435
Bronstein MM, Bronstein AM, Kimmel R, Yavneh I (2006) Multigrid multidimensional scaling. Numer Linear Algebra Appl 13:149–171
Pighin F, Heaker J, Lischinski D, Szeliski R, Salesin DH (1998) Synthesizing realistic facial expression from photographs. In: ACM SIGGRAPH, pp 75–84
Noh J, Neumann U (2001) Expression cloning. In: ACM SIGGRAPH, pp 277–288
Quan W, Matuszewski BJ, Shark L-K, Ait-Boudaoud D (2009) Facial expression biometrics using statistical shape models. EURASIP J Adv Signal Process 1–17
Ruechert D, Sonoda L, Hayes C, Hill D, Leach M, Hawkes D (1999) Nonrigid registration using free-form deformation: application to breast MR images. IEEE Trans Med Imaging 18(8):712–721
Sandbach G, Zafeiriou S, Pantic M, Rueckert D (2011) A dynamic approach to the recognition of 3-D facial expressions and their temporal models. In: IEEE conference on automatic face and gesture recognition, pp 406–413
Sandbach G, Zafeiriou S, Pantic M, Rueckert D (2012) Recognition of 3-D facial expression. Image Vis Comput 30(10):762–773
Bookstein FL (1989) Principal warps: thin-plate splines and the decomposition of deformation. IEEE Trans Pattern Anal Mach Intell 11(6):567–585
Lee S, Wolberd G, Shin S (1997) Scattered data interpolation with multilevel B-splines. IEEE Trans Vis Comput Graph 3(3):228–244
Dhillon IS, Guan Y, Kulis B (2007) Weighted graph cuts without eigenvectors: a multilevel approach. IEEE Trans Pattern Anal Mach Intell 29(11):1–14
Qiu D, May S, Nuchter A (2009) GPU-accelerated nearest neighbor search for 3D registration. In: Proceedings of the 7th international conference on computer vision systems: computer vision systems, pp 194–203
Yin L, Wei X, Sun Y, Wang J, Rosato M (2006) A 3D facial expression database for facial behaviour research. In: 7th international conference on automatic face and gesture recognition, pp 211–216
Moreno AB, Sanchez A (2004) GavabDB: a 3D face database. In: COST workshop on biometrics on internet: fundamentals, advances and applications, pp 77–82
Moghaddam B, Pentland A (1997) Probabilistic visual learning for object representation. IEEE Trans Pattern Anal Mach Intell 19(6):696–710
Phillips PJ (1998) Support vector machines applied to face recognition. Adv Neural Inf Process Syst 11:803–809
Rizvi SA, Philips PJ, Moon H (1998) The FERET verification testing protocol for face recognition algorithms. In: IEEE international conference on automatic face and gesture recognition
Moreno AB, Sanchez A, Velez J, Diaz F (2005) Face recognition using 3D local geometrical features: PCA vs SVM. In: International symposium on image and signal processing and analysis
Mahoor MH, Abdel-Mottaleb M (2009) Face recognition based on 3D ridge images obtained from range data. Pattern Recognit 42(3):445–451
Berrett S, Bimbo A, Pala P (2006) 3D face recognition by modeling the arrangement of concave and convex regions. In: Adaptive multimedia retrieval, pp 108–118
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The authors would like to acknowledge Dr. Lijun Yin from Binghamton University (USA) for making available to the BU-3DFE database.
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Quan, W., Matuszewski, B.J. & Shark, LK. Statistical shape modelling for expression-invariant face analysis and recognition. Pattern Anal Applic 19, 765–781 (2016). https://doi.org/10.1007/s10044-014-0439-x
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DOI: https://doi.org/10.1007/s10044-014-0439-x