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Trends in Machine and Human Face Recognition

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Advances in Face Detection and Facial Image Analysis

Abstract

Face recognition (FR) is a natural and intuitive way for human beings to identify or verify or at least get familiar and interact with other members of the community. Hence, human beings expect and endeavor to develop similar competency in machine recognition of human faces. Due to the rapid increase in computing power in recent decades and the need to automate the FR tasks for many applications, researchers from diverse areas like cognitive and computer sciences are making efforts in understanding how humans and machines recognize human faces respectively. Its application is innumerable (like access control, surveillance, social interactions, e-commerce, just to name a few). In this chapter we will review two aspects of FR: machine recognition of faces and how human beings recognize human faces. We will also discuss the recent benchmark studies, their protocols and databases for FR and psychophysical studies of FR abilities of human beings.

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References

  1. M. Turk, A. Pentland, Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)

    Article  Google Scholar 

  2. Y. Taigman, M. Yang, M. Ranzato, L. Wolf, Deepface: closing the gap to human-level performance in face verification, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Columbus, OH, 2014, pp. 1701–1708

    Google Scholar 

  3. Y. Sun, X. Wang, X. Tang, Deep learning face representation from predicting 10,000 classes, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Columbus, OH, 2014, pp. 1891–1898

    Google Scholar 

  4. P.J. Phillips, Face & ocular challenges. Presentation (2010), http://www.cse.nd.edu/BTAS_10/BTAS_Jonathon_Phillips_Sep_2010_FINAL.pdf

  5. P. Grother, M. Ngan, Face recognition vendor test (FRVT 2013) performance of face identification algorithms. Technical Report (2013), http://www.biometrics.nist.gov/cs_links/face/frvt/frvt2013/NIST_8009.pdf

  6. S. Liao, Z. Lei, D. Yi, S.Z. Li, A benchmark study of large-scale unconstrained face recognition, in IEEE International Joint Conference on Biometrics, Clearwater, FL, 2014, pp. 1–8

    Google Scholar 

  7. A.J. O’Toole, D.A. Roark, H. Abdi, Recognizing moving faces: a psychological and neural synthesis. Trends Cogn. Sci. 6(6), 261–266 (2002)

    Article  Google Scholar 

  8. W.A. Bainbridge, P. Isola, A. Oliva, The intrinsic memorability of face photographs. J. Exp. Psychol. Gen. 4(142), 1323–1334 (2013)

    Article  Google Scholar 

  9. T.A. Busey, Formal models of familiarity and memorability in face recognition, in Computational, Geometric, and Process Perspectives on Facial Cognition: Contexts and Challenges, ed. by M.J. Wenger, J.T. Townsend (Lawrence Erlbaum Associates Publishers, Mahwah, 2001)

    Google Scholar 

  10. S. Georghiades, P.N. Belhumeur, D. Kriegman, From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 643–660 (2001)

    Article  Google Scholar 

  11. L. Zhang, D. Samaras, Face recognition from a single training image under arbitrary unknown lighting using spherical harmonics. IEEE Trans. Pattern Anal. Mach. Intell. 28(3), 351–363 (2006)

    Article  Google Scholar 

  12. S. Vural, Y. Mae, H. Uvet, T. Arai, Illumination normalization for outdoor face recognition by using ayofa-filters. J. Pattern Recognit. Res. 6(1), 1–18 (2011)

    Article  Google Scholar 

  13. X. Zhao, S.K. Shah, I.A. Kakadiaris, Illumination alignment using lighting ratio: application to 3D-2D face recognition, in Proceedings of International Conference on Automatic Face Gesture Recognition, Shanghai, 2013, pp. 1–6

    Google Scholar 

  14. L. Wolf, T. Hassner, I. Maoz, Face recognition in unconstrained video with matched background similarity, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Colorado Springs, 2011, pp. 529–534

    Google Scholar 

  15. P.J. Phillips, H. Moon, S. Rizvi, P. Rauss, The FERET evaluation methodology for face recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090–1104 (2000)

    Article  Google Scholar 

  16. The Face Recognition Technology (FERET) Normalization (2005), http://www.cs.colostate.edu/evalfacerec/data/normalization.html

  17. C. Ding, D. Tao, A comprehensive survey on pose-invariant face recognition. CoRR abs/1502.04383 (2015), http://www.arxiv.org/abs/1502.04383

  18. X. Zhang, Y. Gao, Face recognition across pose: a review. Pattern Recogn. 42, 2876–2896 (2009)

    Article  Google Scholar 

  19. A.M. Martinez, Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Trans. Pattern Anal. Mach. Intell. 24(6), 748–763 (2002)

    Article  Google Scholar 

  20. B. Mandal, X.D. Jiang, A. Kot, Verification of human faces using predicted eigenvalues, in 19th International Conference on Pattern Recognition, Tempa, FL, 2008, pp. 1–4

    Google Scholar 

  21. J. Leibo, Q. Liao, T. Poggio, Subtasks of unconstrained face recognition, in International Joint Conference on Computer Vision, Imaging and Computer Graphics, Lisbon, vol. 2, 2014, pp. 113–121

    Google Scholar 

  22. P.J. Phillips, P.J. Flynn, T. Scruggs, K.W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, W. Worek, Overview of the face recognition grand challenge, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, 2005, pp. 947–954

    Google Scholar 

  23. B. Mandal, W. Zhikai, L. Li, A. Kassim, Evaluation of descriptors and distance measures on benchmarks and first-person-view videos for face identification, in International Workshop on Robust Local Descriptors for Computer Vision, Singapore, 2014, pp. 585–599

    Google Scholar 

  24. X.D. Jiang, B. Mandal, A. Kot, Eigenfeature regularization and extraction in face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 383–394 (2008)

    Article  Google Scholar 

  25. M. Kawulok, J. Szymanek, Precise multi-level face detector for advanced analysis of facial images. IET Image Process. 6(2), 95–103 (2012)

    Article  MathSciNet  Google Scholar 

  26. C. Zhang, Z. Zhang, A survey of recent advances in face detection. Technical Report MSR-TR-2010-66, http://www.research.microsoft.com/pubs/132077/facedetsurvey.pdf

  27. B. Mandal, S. Ching, L. Li, V. Chandrasekha, C. Tan, J.-H. Lim, A wearable face recognition system on Google glass for assisting social interactions, in Third International Workshop on Intelligent Mobile and Egocentric Vision, Singapore, 2014, pp. 419–433

    Google Scholar 

  28. W. Zhao, R. Chellappa, P.J. Phillips, A. Rosenfeld, Face recognition: a literature survey. ACM Comput. Surv. 35(4), 399–458 (2003)

    Article  Google Scholar 

  29. B. Klare, A. Jain, On a taxonomy of facial features, in Proceedings of International Conference on Biometrics: Theory, Applications and Systems (2010), pp. 1–8

    Google Scholar 

  30. H. Zhou, A. Mian, L. Wei, D. Creighton, M. Hossny, S. Nahavandi, Recent advances on singlemodal and multimodal face recognition: a survey. IEEE Trans. Hum. Mach. Syst. 44(6), 701–716 (2014)

    Article  Google Scholar 

  31. P. Belhumeur, Ongoing challenges in face recognition, in Frontiers of Engineering: Reports on Leading-Edge Engineering (2006), pp. 5–14

    Google Scholar 

  32. X. Zou, J. Kittler, K. Messer, Illumination invariant face recognition: a survey, in Proceedings of International Conference on Biometrics: Theory, Applications, and Systems (2007), pp. 1–8

    Google Scholar 

  33. Y. Fu, G. Guo, T.S. Huang, Age synthesis and estimation via faces: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 32(11), 1955–1976 (2010)

    Article  Google Scholar 

  34. A. Jain, B. Klare, U. Park, Face recognition: some challenges in forensics, in Proceedings of International Conference on Automatic Face Gesture Recognition and Workshops (2011), pp. 726–733

    Google Scholar 

  35. G. Huang, M. Ramesh, T. Berg, E.L. Miller, Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical Report 07-49 (University of Massachusetts, Amherst, 2007)

    Google Scholar 

  36. T. Poggio, J. Mutch, F. Anselmi, J. Leibo, L. Rosasco, A. Tacchetti, The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work). MIT-CSAIL-TR-2012-035

    Google Scholar 

  37. Q. Liao, J. Leibo, T. Poggio, Learning invariant representations and applications to face verification, in Neural Information Processing Systems Foundation, Inc., Harrahs and Harveys, Lake Tahoe, USA (2013), pp. 1–9

    Google Scholar 

  38. S.Z. Li, A.K. Jain (eds.), Handbook of Face Recognition, 2nd edn. (Springer, Berlin, 2011)

    MATH  Google Scholar 

  39. J. Barr, K. Bowyer, P. Flynn, S. Biswas, Face recognition from video: a review. Int. J. Pattern Recognit. Artif. Intell. 26(5), (2012), DOI: 10.1142/S0218001412660024

    Google Scholar 

  40. F. Anselmi, J. Leibo, L. Rosasco, J. Mutch, A. Tacchetti, T. Poggio, Unsupervised learning of invariant representations in hierarchical, architectures. arXiv preprint arXiv:1311.4158 (2013)

    Google Scholar 

  41. M. Riesenhuber, T. Poggio, Hierarchical models of object recognition in cortex. Nat. Neurosci. 2(11), 1019–1025 (1999)

    Article  Google Scholar 

  42. T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, T. Poggio, Robust object recognition with cortex-like mechanisms. IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 411–426 (2007)

    Article  Google Scholar 

  43. Y. LeCun, Y. Bengio (eds.), Convolutional networks for images, speech, and time series, in The Handbook of Brain Theory and Neural Networks, ACM Digital Library (1995)

    Google Scholar 

  44. Q. Liao, J.Z. Leibo, Y. Mroueh, T. Poggio, Can a biologically-plausible hierarchy effectively replace face detection, alignment and recognition pipelines? arXiv:1311.4082v3 [cs.CV], no. 003 (2013)

    Google Scholar 

  45. E. Hubel, T. Wiesel, Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. 160(1), 106 (1962)

    Google Scholar 

  46. Labeled faces in the wild (LFW) results (2015), http://www.vis-www.cs.umass.edu/lfw/results.html

  47. L. Rowden, B. Klare, J. Klontz, A.K. Jain, Video-to-video face matching: establishing a baseline for unconstrained face recognition, in IEEE 6th International Conference on Biometrics: Theory, Applications and Systems, Washington, DC, 2013, pp. 1–8

    Google Scholar 

  48. Instagram, Online social network through images (2015), https://www.instagram.com/

  49. Facebook, Online social network (2015), https://www.facebook.com/

  50. A. Ishai, L.G. Ungerleider, A. Martin, J.L. Schouten, J.V. Haxby, Distributed representations of objects in the human ventral visual pathway. Proc. Natl. Acad. Sci. 96, 9379–9384 (1999)

    Article  Google Scholar 

  51. K. Lander, L. Chuang, Why are moving faces easier to recognize? Vis. Cogn. 12(3), 429–442 (2005)

    Article  Google Scholar 

  52. W.A. Bainbridge, P. Isola, I. Blank, A. Oliva, Establishing a database for studying human face photograph memory, in Proceedings of the 34th Annual Conference of the Cognitive Science Society, Austin, TX, 2012, pp. 1302–1307

    Google Scholar 

  53. Face Recognition Homepage Databases (2015), http://www.face-rec.org/databases/

  54. U. Park, A.K. Jain, A. Ross, Face recognition in video: adaptive fusion of multiple matchers, in IEEE Computer Workshop on Biometrics, Minneapolis, 2007, pp. 1–8

    Google Scholar 

  55. H. Li, G. Hua, X. Shen, Z. Lin, J. Brandt, Eigen-PEP for video face recognition, in Asian Conference on Computer Vision, Singapore, 2014, pp. 17–33

    Google Scholar 

  56. Y. Chen, V. Patel, S. Shekhar, R. Chellappa, P. Phillips, Video-based face recognition via joint sparse representation, in 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, Shanghai, 2013, pp. 1–8

    Google Scholar 

  57. R. Chellappa, J. Ni, V. M. Patel, Remote identification of faces: problems, prospects, and progress. Pattern Recogn. Lett. 33(15), 1849–1859 (2012)

    Article  Google Scholar 

  58. B. Mandal, X.D. Jiang, A. Kot, Dimensionality reduction in subspace face recognition, in Sixth IEEE International Conference on Information, Communications & Signal Processing, Singapore, 2007, pp. 1–5

    Google Scholar 

  59. X.D. Jiang, B. Mandal, A. Kot, Complete discriminant evaluation and feature extraction in kernel space for face recognition. Mach. Vis. Appl. (Springer) 20(1), 35–46 (2009)

    Google Scholar 

  60. B. Mandal, H. Eng, Regularized discriminant analysis for holistic human activity recognition. IEEE Intell. Syst. 27(1), 21–31 (2012)

    Article  Google Scholar 

  61. X.D. Jiang, B. Mandal, A. Kot, Enhanced maximum likelihood face recognition. IEE Electron. Lett. 42(19), 1089–1090 (2006)

    Article  Google Scholar 

  62. Z. Zhu, P. Luo, X. Wang, X. Tang, Deep learning identity preserving face space, in International Conference on Computer Vision, Washington, DC, 2013, pp. 113–120

    Google Scholar 

  63. Y. Sun, X. Wang, X. Tang, Hybrid deep learning for face verification, in IEEE International Conference on Computer Vision, Sydney, 2013, pp. 1489–1496

    Google Scholar 

  64. T. Ahonen, A. Hadid, M. Pietikainen, Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)

    Article  MATH  Google Scholar 

  65. Face Recognition Technology (FERET) (1996), http://www.nist.gov/itl/iad/ig/feret.cfm

  66. Face Recognition Vendor Test (FRVT) (2015), http://www.nist.gov/itl/iad/ig/frvt-home.cfm

  67. Face Recognition Grand Challenge (FRGC) (2005), http://www.nist.gov/itl/iad/ig/frgc.cfm

  68. Multiple Biometric Grand Challenge (MBGC) (2009), http://www.nist.gov/itl/iad/ig/mbgc.cfm

  69. Face and Ocular Challenge Series (FOCS): Good, Bad and the Ugly Database (2015), http://www.nist.gov/itl/iad/ig/focs.cfm

  70. P. Phillips, J. Beveridge, B. Draper, G. Givens, A. O’Toole, D. Bolme, J. Dunlop, Y.M. Lui, H. Sahibzada, S. Weimer, An introduction to the good, the bad and the ugly face recognition challenge problem, in IEEE International Conference on Automatic Face Gesture Recognition and Workshops (2011), pp. 346–353

    Google Scholar 

  71. Multiple Biometrics Evaluation (MBE) (2010), http://www.nist.gov/itl/iad/ig/mbe.cfm

  72. Competition on Face Recognition in Mobile Environment (MOBIO) (2013), https://www.idiap.ch/dataset/mobio

  73. Point and Shoot Face Recognition Challenge (PaSC) (2015), http://www.nist.gov/itl/iad/ig/pasc.cfm

  74. X. Wang, X. Tang, Face photo-sketch synthesis and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(11), 1955–1967 (2009)

    Article  Google Scholar 

  75. W. Zhang, X. Wang, X. Tang, Coupled information-theoretic encoding for face photo-sketch recognition, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Colorado Springs, 2011, pp. 513–520

    Google Scholar 

  76. P. Grother, G.W. Quinn, P.J. Phillips, MBE 2010: report on the evaluation of 2D still-image face recognition algorithms. National Institute of Standards and Technology, NISTIR 7709

    Google Scholar 

  77. P.J. Phillips, W.T. Scruggs, A.J. OToole, P.J. Flynn, K.W. Bowyer, C.L. Schott, M. Sharpe, FRVT 2006 and ICE 2006 large-scale results. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 831–846 (2010)

    Google Scholar 

  78. Face Recognition Vendor Test (FRVT2006) (2006), http://www.nist.gov/itl/iad/ig/frvt-2006.cfm

  79. A. O’Toole, P. Phillips, X. An, J. Dunlop, Demographic effects on estimates of automatic face recognition performance, in IEEE International Conference on Automatic Face Gesture Recognition and Workshops, Santa Barbara, CA 2011, pp. 83–90

    Google Scholar 

  80. C. McCool, S. Marcel, A. Hadid, M. Pietikainen, P. Matejka, J. Cernocky, N. Poh, J. Kittler, A. Larcher, C. Levy, D. Matrouf, J.-F. Bonastre, P. Tresadern, T. Cootes, Bi-modal person recognition on a mobile phone: using mobile phone data, in IEEE International Conference on Multimedia and Expo Workshops, 2012, pp. 635–640

    Google Scholar 

  81. M. Gunther et al., Face recognition evaluation in mobile environment, in International Conference on Biometrics, Madrid, 2013, pp. 1–7

    Google Scholar 

  82. N. Pinto, D. Cox, Beyond simple features: a large-scale feature search approach to unconstrained face recognition, in IEEE Automatic Face and Gesture Recognition, Santa Barbara, CA, 2011, pp. 8–15

    Google Scholar 

  83. F. Samaria, A. Harter, Parameterization of a stochastic model for human face identification, in Proceedings of 2nd IEEE Workshop on Applications of Computer Vision, Sarasota, FL, 1994, pp. 138–142

    Google Scholar 

  84. Face Recognition Databases (2015), http://www.face-rec.org/databases/

  85. P. Viola, M. Jones, Rapid object detection using a boosted cascade of simple features, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1 (2001), pp. 511–518

    Google Scholar 

  86. Labeled Faces in the Wild (LFW) (2015), http://www.vis-www.cs.umass.edu/lfw/

  87. G. Huang, V. Jain, Unsupervised joint alignment of complex images, in IEEE International Conference on Computer Vision, Rio de Janeiro, 2007, pp. 1–8

    Google Scholar 

  88. G. Huang, M. Mattar, H. Lee, E.G. Learned-Miller, Learning to align from scratch, in Advances in Neural Information Processing Systems, vol. 25 (2012), pp. 764–772

    Google Scholar 

  89. D. Chen, X. Cao, L. Wang, F. Wen, J. Sun, Bayesian face revisited: a joint formulation, in European Conference on Computer Vision, Florence, 2012, pp. 566–579

    Google Scholar 

  90. M. Everingham, J. Sivic, A. Zisserman, Taking the bite out of automated naming of characters in tv video. Image Vis. Comput. 27(5), 545–559 (2009)

    Article  Google Scholar 

  91. D. Ramanan, S. Baker, S. Kakade, Leveraging archival video for building face datasets, in IEEE International Conference on Computer Vision (2007), pp. 1–8

    Google Scholar 

  92. A. Dantcheva, C. Chen, A. Ross, Can facial cosmetics affect the matching accuracy of face recognition systems? in 2012 IEEE 5th International Conference on Biometrics: Theory, Applications and Systems (BTAS) (2012), pp. 391–398

    Google Scholar 

  93. C. Chen, A. Dantcheva, A. Ross, Automatic facial makeup detection with application in face recognition, in International Conference on Biometrics (IEEE, Madrid, 2013), pp. 1–8

    Google Scholar 

  94. S. Setty et al., Indian movie face database: a benchmark for face recognition under wide variations, in National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, Jodhpur, 2013, pp. 726–733

    Google Scholar 

  95. M. Demirkus, J.J. Clark, T. Arbel, Robust semi-automatic head pose labeling for real-world face video sequences. Multimedia Tools Appl., 70(1), 495–523 (2014)

    Article  Google Scholar 

  96. M.K. Hasan, C.J. Pal, Experiments on visual information extraction with the faces of wikipedia, in Proceedings of the 28th AAAI Conference on Artificial Intelligence, 27–31 July 2014, Québec City, QC, 2014, pp. 51–58

    Google Scholar 

  97. H.-W. Ng, S. Winkler, A data-driven approach to cleaning large face datasets, in 2014 IEEE International Conference on Image Processing (ICIP), 2014, pp. 343–347

    Google Scholar 

  98. M.C. Frank, E. Vul, S.P. Johnson, Development of infants’ attention to faces during the first year. Cognition 110(2), 160–170 (2009)

    Article  Google Scholar 

  99. R.L. Buckner, The serendipitous discovery of the brain’s default network. NeuroImage 62(2), 1137–1145 (2012)

    Article  Google Scholar 

  100. B. Mandal, X. Jiang, H. Eng, A. Kot, Prediction of eigenvalues and regularization of eigenfeatures for human face verification. Pattern Recogn. Lett. 31(8), 717–724 (2010)

    Article  Google Scholar 

  101. K. Lander, L. Chuang, L. Wickham, Recognizing face identity from natural and morphed smiles. Q. J. Exp. Psychol. 59(5), 801–808 (2006)

    Article  Google Scholar 

  102. A. O’Toole, D. Roark, Dynamic Faces: Memory for Moving Faces (The MIT Press, Cambridge, 2011)

    Google Scholar 

  103. K.S. Pilz, I.M. Thornton, H.H. Bülthoff, A search advantage for faces learned in motion. Exp. Brain Res. 171(4), 436–447 (2005)

    Article  Google Scholar 

  104. M.J. Tarr, A.S. Georghiades, C.D. Jackson, Identifying faces across variations in lighting: psychophysics and computation. ACM Trans. Appl. Percept. 5(2), 10:1–10:25 (2008)

    Google Scholar 

  105. J.V. Haxby, E.A. Hoffman, M.I. Gobbini, The distributed human neural system for face perception. Trends Cogn. Sci. 4(6), 223–233 (2000)

    Article  Google Scholar 

  106. P. Sinha, Qualitative Representations for Recognition (Springer, Berlin, 2002), pp. 249–262

    MATH  Google Scholar 

  107. G. Wallis, B.T. Backus, M. Langer, Learning illumination- and orientation-invariant representations of objects through temporal associations. J. Vis. 9(7), 1–8 (2009)

    Article  Google Scholar 

  108. The Science of Why No One Agrees on the Color of This Dress (2015), http://www.wired.com/2015/02/science-one-agrees-color-dress

  109. D. Purves, Brains: How They Seem to Work (Pearson, Financial Times Press, New York, 2010)

    Google Scholar 

  110. D. Purves, R. Cabeza, S.A. Huettel, K.S. LaBar, M.L. Platt, M.G. Woldorff, Principles of Cognitive Neuroscience, 2nd edn., Sunderland, MA 01375-0407, USA, (Sinauer Associates, 2012)

    Google Scholar 

  111. R. Blake, M. Shiffrar, Perception of human motion. Annu. Rev. Psychol. 58, 47–73 (2007)

    Article  Google Scholar 

  112. M. Kamachi, V. Bruce, S. Mukaida, J. Gyoba, S. Yoshikawa, S. Akamatsu, Dynamic properties influence the perception of facial expressions. Perception 30(7), 875–887 (2001)

    Article  Google Scholar 

  113. D. Pitcher, D.D. Dilks, R.R. Saxe, C. Triantafyllou, N. Kanwisher, Differential selectivity for dynamic versus static information in face-selective cortical regions. NeuroImage 56(4), 2356–2363 (2011)

    Article  Google Scholar 

  114. J. Schultz, M. Brockhaus, H.H. Bülthoff, K.S. Pilz, What the human brain likes about facial motion. Cereb. Cortex 23, 1167–1178 (2012)

    Article  Google Scholar 

  115. C.P. Said, J.V. Haxby, A. Todorov, Brain systems for assessing the affective value of faces. Philos. Trans. R. Soc. B 366, 1660–1670 (2011)

    Article  Google Scholar 

  116. M.A. Giese, T. Poggio, Neural mechanisms for the recognition of biological movements. Nat. Rev. Neurosci. 4, 179–192 (2003)

    Article  Google Scholar 

  117. S. Rigby, B. Stoesz, L. Jakobson, How dynamic facial cues, stimulus orientation and processing biases influence identity and expression interference. J. Vis. 13(9), 413–418 (2013)

    Article  Google Scholar 

  118. X.D. Jiang, B. Mandal, A. Kot, Face recognition based on discriminant evaluation in the whole space, in IEEE 32nd International Conference on Acoustics, Speech and Signal Processing, Honolulu, Hawaii, 2007, pp. 245–248

    Google Scholar 

  119. J.S. Winston, R. Henson, M.R. Fine-Goulden, R.J. Dolan, fMRI-adaptation reveals dissociable neural representations of identity and expression in face perception. J. Neurophysiol. 92(3), 1830–1839 (2004)

    Google Scholar 

  120. S. Bentin, J.M. DeGutis, M. D’Esposito, L.C. Robertson, Too many trees to see the forest: Performance, event-related potential, and functional magnetic resonance imaging manifestations of integrative congenital prosopagnosia. J. Cogn. Neurosci. 19(1), 132–146 (2007)

    Article  Google Scholar 

  121. B.C. Duchaine, H. Parker, K. Nakayama, Normal recognition of emotion in a prosopagnosic. Perception 32, 827–838 (2003)

    Article  Google Scholar 

  122. N.G. Xiao, P.C. Quinn, L. Ge, K. Lee, Elastic facial movement influences part-based but not holistic processing. J. Exp. Psychol. Hum. Percept. Perform. 39(5), 1457–1467 (2013)

    Article  Google Scholar 

  123. M.T. Posamentier, H. Abdi, Processing faces and facial expressions. Neuropsychol. Rev. 13(3), 113–143 (2003)

    Article  Google Scholar 

  124. W.C.D. Souza, S. Eifuku, R. Tamura, H. Nishijo, T. Ono, Differential characteristics of face neuron responses within the anterior superior temporal sulcus of macaques. J. Neurophysiol. 94, 1252–1266 (2005)

    Article  Google Scholar 

  125. U. Park, Y. Tong, A.K. Jain, Age-invariant face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 947–954 (2010)

    Article  Google Scholar 

  126. B. Mandal, X.D. Jiang, A. Kot, Multi-scale feature extraction for face recognition, in IEEE International Conference on Industrial Electronics and Applications, Singapore, 2006, pp. 1–6

    Google Scholar 

  127. T. Igarashi, K. Nishino, S.K. Nayar, The appearance of human skin. Technical Report, Columbia University, New York (2005)

    Google Scholar 

  128. J. Suo, F. Min, S. Zhu, S. Shan, X. Chen, A multi-resolution dynamic model for face aging simulation, in IEEE Computer Vision and Pattern Recognition, Minneapolis, MN, 2007, pp. 1–8

    Google Scholar 

  129. R. Lim, M.R. Sayed, B. Mandal, K.T. Ma, L. Li, J.H. Lim, Evaluating human performance in dynamic perspective invariant face recognition (accepted), in 11th Asia-Pacific Conference on Vision, Singapore, 2015

    Google Scholar 

  130. G. Kreiman, Kreiman’s lab (2015), http://www.klab.tch.harvard.edu/publications/publications.html

  131. K. Patterson, A.D. Baddeley, When face recognition fails. J. Exp. Psychol. Hum. Learn. Mem. 3(4), 406–417 (1977)

    Article  Google Scholar 

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  1. Institute for Infocomm Research, A*STAR, 1 Fusionopolis Way, #21-01, Connexis (South Tower), Singapore, 138632, Singapore

    Bappaditya Mandal, Rosary Yuting Lim, Peilun Dai, Mona Ragab Sayed, Liyuan Li & Joo Hwee Lim

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  1. Bappaditya Mandal
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  2. Rosary Yuting Lim
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  3. Peilun Dai
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  4. Mona Ragab Sayed
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  5. Liyuan Li
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  6. Joo Hwee Lim
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Correspondence to Bappaditya Mandal .

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Editors and Affiliations

  1. Automatic Control, Silesian University of Technology, Kawulok, Poland

    Michal Kawulok

  2. Computer Science, Louisiana State University in Shreveport, Shreveport, Louisiana, USA

    M. Emre Celebi

  3. Silesian University of Technology, Gliwice, Poland

    Bogdan Smolka

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© 2016 Springer International Publishing Switzerland

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Mandal, B., Lim, R.Y., Dai, P., Sayed, M.R., Li, L., Lim, J.H. (2016). Trends in Machine and Human Face Recognition. In: Kawulok, M., Celebi, M., Smolka, B. (eds) Advances in Face Detection and Facial Image Analysis. Springer, Cham. https://doi.org/10.1007/978-3-319-25958-1_7

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  • DOI: https://doi.org/10.1007/978-3-319-25958-1_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25956-7

  • Online ISBN: 978-3-319-25958-1

  • eBook Packages: EngineeringEngineering (R0)

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