Skip to main content
SpringerLink
Log in

Face Recognition in Humans and Machines

  • Chapter
Handbook of Remote Biometrics

Part of the book series: Advances in Pattern Recognition ((ACVPR))

  • 2143 Accesses

Abstract

The study of human face recognition by psychologists and neuroscientists has run parallel to the development of automatic face recognition technologies by computer scientists and engineers. In both cases, there are analogous steps of data acquisition, image processing, and the formation of representations that can support the complex and diverse tasks we accomplish with faces. These processes can be understood and compared in the context of their neural and computational implementations. In this chapter, we present the essential elements of face recognition by humans and machines, taking a perspective that spans psychological, neural, and computational approaches. From the human side, we overview the methods and techniques used in the neurobiology of face recognition, the underlying neural architecture of the system, the role of visual attention, and the nature of the representations that emerges. From the computational side, we discuss face recognition technologies and the strategies they use to overcome challenges to robust operation over viewing parameters. Finally, we conclude the chapter with a look at some recent studies that compare human and machine performances at face recognition.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. J. Bodamer. Die prosopagnosie. Arch. Psychiat. Nerv. 179:6–54, 1947.

    Article  Google Scholar 

  2. N. Kanwisher, J. McDermott, and M. Chun. The fusiform face area: a module in human extrastriate cortex specialized for face perception. J. Neurosci. 17:4302–4311, 1997.

    Google Scholar 

  3. J. Barton. Disorders of face perception and recognition. Neurol Clin. 21:521–548, 2003.

    Article  Google Scholar 

  4. A. Damasio. Prosopagnosia. Trends Neurosci. 132–135, 1985.

    Google Scholar 

  5. J. Meadows. The anatomical basis of prosopagnosia. J. Neurol. Neurosurg. Psychi., 37: 489–501, 1974.

    Article  Google Scholar 

  6. E. DeRenzi. Prosopagnosia in two patients with CT scan evidence of damage confined to the right hemisphere. Neuropsychologia, 24:385–389. 1986.

    Article  Google Scholar 

  7. T. Landis, J. Cummings, L. Christen, J. Bogen, and H-G. Imbof. Are unilateral right posterior cerebral lesions sufficient to cause prosopagnosia? Clinical and radiological findings in six additional patients. Cortex, 22:243–52, 1986.

    Google Scholar 

  8. V. Bruce and A.W. Young. Understanding face recognition. Br. J. Psychol. 77(3):305–327, 1986.

    Google Scholar 

  9. R. Bruyer, C. Laterre, X. Seron, P. Feyereisen, E. Strypstein, E. Pierrard, and D. Rectem. A case of prosopagnosia with some preserved covert remembrance of similar faces. Brain Cogn. 2:257–284, 1983.

    Article  Google Scholar 

  10. J. Hornak, E. Rolls, and D. Wade. Face and voice expression identification in patients with emotional and behavioral changes following ventral frontal lobe damage. Neuropsychologia, 34:173–181, 1996.

    Article  Google Scholar 

  11. F. Parry, A. Young, J. Saul, and A. Moss. Dissociable face processing impairments after brain injury. J. Clin. Exp. Neuropsychol. 13:545–558, 1991.

    Article  Google Scholar 

  12. E. Shuttleworth, V. Syring, and N. Allen. Further observations on the nature of prosopagnosia. Brain Cogn. 1:307–322, 1982.

    Article  Google Scholar 

  13. D. Tranel, A. Damasio, and H. Damasio. Intact recognition of facial expression, gender, and age in patients with impaired recognition of face identity. Neurology, 38:690–696, 1988.

    Google Scholar 

  14. A. Calder, A. Young, D. Rowland, D. Perrett, J. Hodges, and H. Etcoff. Facial emotion recognition after bilateral amygdala damage: Differentially severe impairment of fear. Cogn. Neuropsychol. 13:699–745, 1996.

    Article  Google Scholar 

  15. G. Humphreys, N. Donnelly, and M. Riddoch. Expression is computed separately from facial identity, and it is computed separately for moving and static faces: Neuropsychological evidence. Neuropsychologia, 31:173–181, 1993.

    Article  Google Scholar 

  16. J. Kurucz and J. Feldmar. Prosopo-affective agnosia as a symptom of cerebral organic brain disease. J. Am. Geriatr. Soc. 27:91–95, 1979.

    Google Scholar 

  17. J. Kurucz, J. Feldmar, and W. Werner. Prosopo-affective agnosia associated with chronic organic brain syndrome. J. Am. Geriatr. Soc. 27:225–230, 1979.

    Google Scholar 

  18. A. Young. Face recognition impairments. Philos. Trans. Roy. Soc. Lond. 335B:47–54, 1992.

    Article  Google Scholar 

  19. A. Calder and A. Young. Understanding the recognition of facial identity and facial expression. Nature Rev. Neurosci. 6:641–651, 2005.

    Article  Google Scholar 

  20. D.H. Hubel and T. Wiesal. Receptive fields, binocular interactiion, and functional architecture in cat’s visual cortex. J. Physiol. 160:106–154, 1962.

    Google Scholar 

  21. J. Movshon, and W. Newsome. Neural foundations of visual motion perception. Curr. Direct. Psychol. Sci. 1:35–39, 1992.

    Article  Google Scholar 

  22. S. Zeki. Color coding in the cerebral cortex: The reaction of cells in monkey visual cortex to wavelengths and colours. Neuroscience, 9:741–765, 1983.

    Article  Google Scholar 

  23. C. Gross, C. Rocha-Miranda, and D. Bender. Visual properties of neurons in inferotemporal cortex of monkeys. J. Neurophysiol. 35:96–111, 1972.

    Google Scholar 

  24. C. Gross. Brain, Vision, and Memory. MIT Press, Cambridge, MA, 1998.

    Google Scholar 

  25. M. Hasselmo, E. Rolls, and G. Baylis. Object-centered encoding by face-selective neurons in the cortex in the superior temporal sulcus of the monkey. Exp. Brain Res. 75:417–429, 1989.

    Article  Google Scholar 

  26. J. Hietanen, D. Perrett, M. Oram, P. Benson, and W. Dittrich. The effects of lighting conditions on responses of cells selective for face views in the macaque temporal cortex. Exp. Brain Res. 89:157–171, 1992.

    Article  Google Scholar 

  27. D. Perrett, J. Hietanen, M. Oram, and P. Benson. Organization and function of cells responsive to faces in temporal cortex. Phil. Trans. Roy. Soc. Lond. B Biol. Sci. 335: 23–30, 1992.

    Article  Google Scholar 

  28. E. Eger, S. Schweinberger, R. Dolan, and R. Hensond. Familiarity enhances invariance of face representations in human ventral visual cortex: fMRI evidence. Neurolmage, 26:1128–1139, 2005.

    Article  Google Scholar 

  29. K. Grill-Spector, T. Kushnir, S. Edelman, G. Avidan, Y. Itzchak, and R. Malach. Differential processing of objects under various viewing conditions in the human lateral occipital complex. Neuron, 24, 187–203, 1999.

    Article  Google Scholar 

  30. J. Haynes, and G. Rees. Decoding mental states from brain activity in humans. Nature Rev. Neurosci. 7:523–534, 2006.

    Article  Google Scholar 

  31. A.J. O’Toole, F. Jiang, H. Abdi, N. Penard, J. Dunlop, and M. Parent. Theoretical, statistical, and practical perspectives on pattern-based classification approaches to the analysis of functional neuroimaging data in ventral temporal cortex. J. Cogn. Neurosci. 19:1735–1752, 2007.

    Article  Google Scholar 

  32. J.V. Haxby, E.A. Hoffman, and M.I. Gobbini. The distributed human neural system for face perception. Trends Cogn. Sci. 20(6): 223–233, 2000.

    Article  Google Scholar 

  33. I. Gauthier, M.J. Tarr, A.W. Anderson, P. Skudlarski, and J.C. Gore. Activation of the middle fusiform face area increases with expertise recognizing novel objects. Nature Neurosci. 2: 568–573, 1999.

    Article  Google Scholar 

  34. J.V. Haxby, M.I. Gobbini, M.L. Furey, A. Ishai, J.L. Shouten and J.L.Pietrini. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293: 2425–2430, 2001.

    Article  Google Scholar 

  35. M. Spiridon and N. Kanwisher. How distributed is visual category information in human occipito-temporal cortex? An fMRI study. Neuron, 35: 1157, 2002.

    Article  Google Scholar 

  36. T. Carlson, P. Schrater, and S. He. Patterns of activity in the categorical representations of objects. J. Cogn. Neurosci. 15:704–717, 2003.

    Article  Google Scholar 

  37. D. Cox and R. Savoy. Functional magnetic resonance imaging (fMRI) “Brain Reading”: Detecting and classifying distributed patterns of FMRI activity in human visual cortex. Neuroimage 19:261–270, 2003.

    Article  Google Scholar 

  38. Y. Kamitani and F. Tong. Decoding the visual and subjective contents of the human brain. Nature Neurosci. 8:679–685, 2005.

    Article  Google Scholar 

  39. A.J. O’Toole, F. Jiang, H. Abdi, and J. Haxby. Partially distributed representations of objects and faces in ventral temporal cortex. J. Cogn. Neurosci. 17:580–590, 2005.

    Article  Google Scholar 

  40. T. Allison, A. Puce and G. McCarthy. Social perception from visual cues: Role of the STS region. Trends Cogn. Sci. 4:267–278, 2000.

    Article  Google Scholar 

  41. E.A. Hoffman and J.V. Haxby. The distinct representations of eye gaze in and identity in the distributed human neural system for face perception. Nature Neurosci. 3: 80–84, 2000.

    Article  Google Scholar 

  42. M. Oram and D.I. Perrett. Integration of form and motion in the anterior superior temporal polysensory area (STPa) of the Macaque monkey. J. Neurophysiol. 76, 109–129, 1996.

    Google Scholar 

  43. D.H. Ballard. Animate vision. Artifi. Intelli. 48:57–86, 1991.

    Article  Google Scholar 

  44. Y. Aloimonos. Purposize, qualitative, active vision. CVGIP: Image Understand. 56(special issue on qualitative, active vision):3–129, July 1992.

    Article  Google Scholar 

  45. M. Tistarelli. Active/space-variant object recognition. Image Vision Comput. 13(3):215–226, 1995.

    Article  Google Scholar 

  46. E. L. Schwartz, D. N. Greve, and G. Bonmassar. Space-variant active vision: definition, overview and examples. Neural Networks 8(7/8):1297–1308, 1995.

    Article  Google Scholar 

  47. C. A. Curcio, K. R. Sloan, R. E. Kalina, and A. E. Hendrickson. Human photoreceptor topography J. Comput. Neurol. 292 (4) : 497–523, 1990.

    Article  Google Scholar 

  48. G. Sandini, G. Metta. Retina- like sensors: Motivations, technology and applications In Sensors and Sensing in Biology and Engineering, T.W. Secomb, F. Barth and P. Humphrey (Eds). Springer-Verlag, Berlin, 2002.

    Google Scholar 

  49. P. J. Burt. Smart sensing in machine vision. In Machine Vision: Algorithms, Architectures, and Systems. Academic Press, New York, 1988.

    Google Scholar 

  50. F. Tong and Z.N. Li. The reciprocal-wedge transform for space-variant sensing. In 4th IEEE Intl. Conference on Computer Vision, pages 330–334, Berlin, 1993.

    Google Scholar 

  51. E. L. Schwartz. Spatial mapping in the primate sensory projection: Analytic structure and relevance to perception. Biol. Cyber. 25, 181–194, 1977.

    Article  Google Scholar 

  52. T.E. Fisher and R.D. Juday. A programmable video image remapper. In Proceedings of SPIE, volume 938, pages 122–128, 1988.

    Google Scholar 

  53. E. Grosso and M. Tistarelli. Log-polar Stereo for Anthropomorphic Robots. In Proc. of 6th European Conference on Computer Vision, pages 299-313, Springer Verlag LNCS 1842, 2000.

    Google Scholar 

  54. J.M. Henderson, C.C. Williams, and R.J. Falk. Eye movements are functional during face learning. Mem. Cogn. 33(1), 98–106, 2005.

    Google Scholar 

  55. A.L. Yarbus. Eye Movements and Vision. Plenum Press, New York, 1967.

    Google Scholar 

  56. Y. Yeshurun and E. L. Schwartz. Shape description with a space-variant sensor: Algorithms for scan-path, fusion and convergence over multiple scans. IEEE Trans. PAMI, PAMI-11:1217–1222, Nov. 1993.

    Google Scholar 

  57. J. Shepherd. Social factors in face recognition. In G. Davies, H. Ellis & J. Shepherd (Eds.). Perceiving and Remembering face, Academic Press, London, 55–79, 1981.

    Google Scholar 

  58. F. K. D. Nahm, A. Perret, D. Amaral, and T. D. Albright. How do monkeys look at faces?. J. Cogn. Neurosci. 9, 611–623, 1997.

    Article  Google Scholar 

  59. M. M. Haith, T. Bergman, and M. J. Moore. Eye contact and face scanning in early infancy. Science, 198, 853–854, 1979.

    Article  Google Scholar 

  60. A. Klin. Eye-tracking of social stimuli in adults with autism. NICHD Collaborative Program of Excellence in Autism, Yale University, New Haven, CT, May 2001.

    Google Scholar 

  61. M. Tistarelli, and E. Grosso. Active vision-based face authentication. Image and Vision Computing: Special issue on Facial Image Analysis, M. Tistarelli ed., vol. 18, no. 4, 299–314, 2000.

    Google Scholar 

  62. M. Bicego, E. Grosso, and M. Tistarelli. On finding differences between faces. in Audio- and Video-based Biometric Person Authentication, T. Kanade, A. Jain, and N.K. Ratha, Eds., vol. LNCS 3546, pp. 329–338. Springer, 2005.

    Google Scholar 

  63. C. Goren, M. Sarty, and P. Wu. Visual following and pattern discrimination of face-like stimuli by newborn infants. Pediatrics 56, 544–549, 1975.

    Google Scholar 

  64. P. Thompson. Margaret Thatcher: A new illusion. Perception 9 : 483–484, 1980.

    Article  Google Scholar 

  65. R.K. Yin. Looking at upside-down faces. J. Exp. Psychol. 81 : 141–145, 1969.

    Article  Google Scholar 

  66. R.E. Galper and J. Hochberg. Recognition memory for photographs of faces. Am. J. Psychol. 84(3), 351–354, 1971.

    Article  Google Scholar 

  67. A.J. O’Toole, T. Vetter, and V. Blanz. Two-dimensional reflectance and three-dimensional shape contributions to face recognition across viewpoint change. Vision Res. 39 : 3145–3155, 1997.

    Article  Google Scholar 

  68. V. Blanz and T. Vetter. A morphable model for the synthesis of 3D faces. In SIGGRAPH’99 Proceedings, ACM: Computer Society Press, 187–194, 1999.

    Google Scholar 

  69. P. Sinha, B.J. Balas, Y. Ostrovsky, R. Russell. Face recognition by humans: 19 results all computer vision researchers should know about. Proceedings of the IEEE, vol. 94, no. 11, pp 1948–1962, 2006.

    Article  Google Scholar 

  70. L. Light, F. Kayra-Stuart, and S. Hollander. Recognition memory for typical and unusual faces. J. Exp. Psychol. Human Learn. Mem. 5 : 212–228, 1979.

    Article  Google Scholar 

  71. T. Valentine. A unified account of the effects of distinctiveness, inversion, and race in face recognition. Quar. J. Exp. Psychol. 43A : 161–204, 1991.

    Google Scholar 

  72. G. Rhodes. Superportraits: Caricatures and Recognition. Psychology Press, Hove, UK, 1997.

    Google Scholar 

  73. R.S. Malpass and J. Kravitz. Recognition for faces of own and other race faces. J. Personality Soc. Psychol. 13 : 330–334, 1969.

    Article  Google Scholar 

  74. J.C. Brigham and P. Barkowitz. Do “They all look alike?” The effects of race, sex, experience and attitudes on the ability to recognize faces. J. Appl. Soc. Psychol. 8:306–318, 1978.

    Article  Google Scholar 

  75. C.A. Meissner and J.C. Brigham. Thirty years of investigating the own-race bias in memory for faces: A meta-analytic review. Psychol Public Policy Law 7: 3–35, 2001.

    Article  Google Scholar 

  76. G. Bryatt and G. Rhodes. Recognition of own-race and other-race caricatures: Implications for models of face recognition. Vision Res. 38:2455–2468, 1998.

    Article  Google Scholar 

  77. D.J. Kelly, P.C. Quinn, A.M Slater, A.M. Lee, L. Ge and O. Pascalis. The other-race effect develops during infancy: Evidence of perceptual narrowing. Psychol. Sci. 18:1084–1089.

    Google Scholar 

  78. P.K. Kuhl, K.A. Williams and F. Lacerdo. Linguistic experience alters phonetic perception in infants by 6 months of age. Science, 255:606–608, 1992.

    Article  Google Scholar 

  79. A.J. O’Toole, D. Roark, and H. Abdi. Recognition of moving faces: a psychological and neural perspective. Trends Cogn. Sci. 6 : 261–266, 2002.

    Article  Google Scholar 

  80. H. Hill and A. Johnston. Categorizing sex and identity from the biological motion of faces. Curr. Biol. 11:880–885, 2001.

    Article  Google Scholar 

  81. B. Knappmeyer, I.M. Thornton and H.H. Bulthoff. The use of facial motion and facial form during the processing of identity. Vision Res. 43:1921–1936, 2003.

    Article  Google Scholar 

  82. W.H. Merigan. P and M pathway specialization in the macaque. In A. Valberg and B.B. Lee, editors. From Pigments to Perception. Plenum, New York, pp. 117–125, 1991.

    Google Scholar 

  83. M. Kirby and L. Sirovich, Application of the Karhunen–Loeve procedure for the characterization of human faces. IEEE Trans. Patt. Anal. Mach. Intelli. 2 (1), 103–108, Jan. 1990.

    Article  Google Scholar 

  84. M. Turk and A. P. Pentland. Eigenfaces for recognition. J. Cogn. Neurosci. 3 (1), 7186, 1991.

    Article  Google Scholar 

  85. B. Schölkopf, A. Smola, and K. R. Müller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10, 1299–1319, 1999.

    Article  Google Scholar 

  86. L. Chengjun. Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE Trans. Patt. Anal. Mach. Intelli. 26(5), 572–581, May 2004.

    Article  Google Scholar 

  87. D. D. Lee and H. S. Seung. Learning the parts of objects by non-negative matrix factorization, Nature, 401, pp. 788–791, 1999.

    Article  Google Scholar 

  88. S.Z. Li, X.W. Hou, and H.J. Zhang, Learning spatially localized, parts-based representation, in CVPR, 2001, pp. 207–212.

    Google Scholar 

  89. H. Yu and J. Yang. A direct lda algorithm for high-dimensional data with application to face recognition. Patt. Recogn. 34, 2067–2070, 2001.

    Article  MATH  Google Scholar 

  90. L. Juwei, K.N. Plataniotis, and A.N. Venetsanopoulos. Face recognition using lda-based algorithms. IEEE Trans. Neural Networks 14(1), 195–200, 2003.

    Article  Google Scholar 

  91. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. Patt. Anal. Mach. Intelli 19(7), 711–720, 1997.

    Article  Google Scholar 

  92. G. Baudat and F. Anouar. Generalized discriminant analysis using a kernel approach. Neural Comput. 12, 2385–2404, 2000.

    Article  Google Scholar 

  93. K.-R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf. An introduction to Kernel-based learning algorithms. IEEE Trans. Neural Networks 12(2), 181–201, 2001.

    Article  Google Scholar 

  94. L. Juwei, K.N. Plataniotis, and A.N. Venetsanopoulos. “Face recognition using kernel direct discriminant analysis algorithms.” IEEE Tran. Neural Networks 14(1), 117–126, 2003.

    Article  Google Scholar 

  95. S. Mika, G. Rätsch, J. Weston, B. Schölkopf, and K.-R. Müller, Fisher discriminant analysis with Kernels, Proc. IEEE Int Workshop Neural Networks for Signal Processing IX, pp.41–48, Aug. 1999.

    Google Scholar 

  96. S. Mika, G. Rätsch, B. Schölkopf, A. Smola, J. Weston, and K.-R. Müller. Invariant Feature Extraction and Classification in Kernel Spaces, Advances in Neural Information Processing Systems 12, MIT Press, Cambridge, MA, 1999.

    Google Scholar 

  97. A. Martinez and A. Kak. PCA versus LDA. IEEE Trans. Patt. Anal. Mach. Intell. 23(2), 228–233, 2001.

    Article  Google Scholar 

  98. A.K. Jain and B. Chandrasekaran. Dimensionality and sample size considerations in pattern recognition practice. in Handbook of Statistics, P. R. Krishnaiah and L. N. Kanal (Eds.) North-Holland, Amsterdam, vol. 2, pp. 835-855, 1987.

    Google Scholar 

  99. S.J. Raudys and A.K. Jain. Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE Trans. Patt. Anal. Mach. Intell. 13(3), 252–264, 1991.

    Article  Google Scholar 

  100. M.S. Bartlett, J.R. Movellan and T.J. Sejnowski. Face recognition by independent component analysis. IEEE Trans. Neural Networks 13(6), 1450–1464, 2002.

    Article  Google Scholar 

  101. L. Chengjun 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 

  102. F. Bach and M. Jordan. Kernel independent component analysis. J. Mach. Learn. Res. 3, 1–48, 2002.

    Article  MathSciNet  Google Scholar 

  103. F.L. Bookstein. Principal warps: Thin-plate splines and the decomposition of deformations. IEEE Trans. Patt. Anal. Mach. Intelli. 11, 567–585, 1989.

    Article  MATH  Google Scholar 

  104. F.Y. Shih and C. Chuang. Automatic extraction of head and face boundaries and facial features. Inf. Sci. 158, 117–130, 2004.

    Article  Google Scholar 

  105. K. Sobottka and I. Pitas. A fully automatic approach to facial feature detection and tracking. in J. Bigün, G. Chollet, G. Borgefors (eds.), Audio- and Video-Based Biometric Person Authentication, LNCS, vol.1206, pp.77-84, Springer Verlag, Berlin, 1997.

    Chapter  Google Scholar 

  106. M. Zobel, A. Gebhard, D. Paulus, J. Denzler and H. Niemann. Robust facial feature localization by coupled features. in 4th IEEE Int. Conf. on Automatic Face and Gesture Recognition, Grenoble, France, 2000.

    Google Scholar 

  107. L. Wiskott, J. M. Fellous, and C. V. der Malsburg. Face recognition by elastic bunch graph matching. IEEE Trans. Patt. Anal. Mach. Intelli. 19, 775–779, 1997.

    Article  Google Scholar 

  108. Z. Xue, S.Z. Li and E.K. Teoh. Bayesian shape model for facial feature extraction and recognition. Patt. Recogn. 36, 2819–2833, 2003.

    Article  MATH  Google Scholar 

  109. A. Pentland, B. Moghaddam and T. Starner. View-based and modular eigenspaces for face recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 84–91, 21–23 June 1994, Seattle, Washington, USA.

    Google Scholar 

  110. J. Lange, C. V. D. Malsburg, R. P. Wurtz and W. Konen. Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. Compu. 42(3), 300–311, March 1993.

    Article  Google Scholar 

  111. L. Wiskott. Phantom faces for face analysis. Patt. Recogn. 30(6), 837–846, 1997.

    Article  Google Scholar 

  112. B. Duc, S. Fischer, and J. Bigün. Face authentication with Gabor information on deformable graphs. IEEE Trans. Image Process. 8(4), 504–516, Apr. 1999.

    Article  Google Scholar 

  113. C. Kotropoulos, A. Tefas, and I. Pitas. Frontal face authentication using morphological elastic graph matching. IEEE Trans. Image Process. 9(4), 555–560, Apr. 2000.

    Article  Google Scholar 

  114. P. T. Jackway and M. Deriche. Scale-space properties of the multiscale morphological dilation-erosion. IEEE Trans. Patt. Anal. Mach. Intelli. 18(1), 38–51, 1996.

    Article  Google Scholar 

  115. A. Tefas, C. Kotropoulos and I. Pitas. Face verification using elastic graph matching based on morphological signal decomposition. Signal Process. 82(6), 833–851, 2002.

    Article  MATH  Google Scholar 

  116. N. Kruger. An algorithm for the learning of weights in discrimination functions using A priori constraints. IEEE Trans. Patt. Anal. Mach. Intelli. 19(7), 764–768, July 1997.

    Article  Google Scholar 

  117. A. Tefas, C. Kotropoulos and I. Pitas. Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication. IEEE Trans. Patt. Anal. Mach. Intelli. 23(7), 735–746, 2001.

    Article  Google Scholar 

  118. K. I. Chang, K. W. Bowyer, and P. J. Flynn. Face Recognition Using 2D and 3D Facial Data. Workshop in Multimodal User Authentication, pp. 25-32, Santa Barbara, California, December. 2003.

    Google Scholar 

  119. Kyong I. Chang, Kevin W. Bowyer and Patrick J. Flynn. An evaluation of multi-modal 2D+3D face biometrics. IEEE Trans. Patt. Anal. Mach. Intelli. 27(4), 619–624, 2005.

    Article  Google Scholar 

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

    Article  Google Scholar 

  121. S. Arca, P. Campadelli, R. Lanzarotti. A face recognition system based on automatically determined facial fiducial points. Patt. Recogn. 39(3), 432–443, March 2006.

    Article  MATH  Google Scholar 

  122. E.G. Llano, H. Mendez-Vazquez, J. Kittler and K. Messer. An illumination insensitive representation for face verification in the frequency domain. In Proceedings of the 18th international Conference on Pattern Recognition – Vol 01, pp. 215-218, (August 20–24, 2006), IEEE Computer Society, Washington, DC.

    Google Scholar 

  123. S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. M. ter Haar Romeny, J. B. Zimmerman, K. Zuiderveld. Adaptive histogram equalization and its variations. CVGIP 39(3):355–368, September 1987.

    Google Scholar 

  124. Y. Adini, Y. Moses and S. Ullman. Face recognition: The problem of compensating for changes in illumination direction. IEEE Trans. Patt. Anal. Mach. Intelli. 19(7):721–732, 1997.

    Article  Google Scholar 

  125. H. W. Jensen. Digital face cloning. In Proceedings SIGGRAPH’2003 Technical Sketch, San Diego, July 2003.

    Google Scholar 

  126. Laiyun Qing, Shiguang Shan, and Xilin Chen. Face relighting for face recognition under generic illumination. ICASSP 2004, pp. 733–736, 2004.

    Google Scholar 

  127. R. Gross and V. Brajovic. An image preprocessing algorithm for illumination invariant face recognition. Proc. of International Conference on Audio- and Video-Based Biometric Person Authentication, pp. 10–18, 2003.

    Google Scholar 

  128. Haitao Wang, Stan Z. Li, Yangsheng Wang, and Weiwei Zhang. Illumination modeling and Normalization for Face Recognition Proceedings of IEEE International Workshop on Analysis and Modeling of Faces and Gestures. Nice, France. 2003.

    Google Scholar 

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

    Google Scholar 

  130. Y. Weiss. Deriving intrinsic images from image sequences. Proc. ICCV,01. Vol. II, pp. 68–75, 2001.

    Google Scholar 

  131. M. Turk, A random walk through eigenspace, IEICE Trans. Information Systems, vol. E84-D, no. 12, pp. 1586-1695, December 2001.

    Google Scholar 

  132. P. J. Phillips, P. J. Flynn, W. T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. J. Worek, Overview of the face recognition grand challenge, In Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 947-954, 2005.

    Google Scholar 

  133. S. M., Lucas. Continuous n-tuple classifier and its application to real-time face recognition. In IEE Proceedings-Vision Image and Signal Processing, vol. 145, no. 5, October 1998, p. 343.

    Article  Google Scholar 

  134. S. M. Lucas. and T. K., Huang. Sequence recognition with scanning N-tuple ensembles. In Proceedings ICPR04 (III) pp 410–413.

    Google Scholar 

  135. B., Raytchev, and H., Murase. Unsupervised recognition of multi-viewface sequences based on pairwise clustering with attraction andrepulsion. Comput. Vision Image Understand. 91(1-2), July–August 2003, pp. 22–52.

    Article  Google Scholar 

  136. B. Raytchev, and H. Murase. VQ-Faces: Unsupervised face recognition from image sequences. In Proceedings ICIP02 (II), pp. 809–812.

    Google Scholar 

  137. B. Raytchev, and H. Murase. Unsupervised face recognition from image sequences. In Proceedings ICIP01(I), pp. 1042–1045.

    Google Scholar 

  138. S. Zhou, V. Krueger, and R. Chellappa. Probabilistic recognition of human faces from video. Comput. Vision Image Understand. 91(1-2), 214–245, July–August 2003.

    Article  Google Scholar 

  139. S.K. Zhou, R. Chellappa, and B. Moghaddam. Visual Tracking and Recognition using appearance-adaptive models in particle filters. Image Process. (11) 1491–1506, November 2004.

    Article  Google Scholar 

  140. Y. Li, S. Gong, and H. Liddell. Modelling faces dynamically across views and over time. In Proceedings IEEE International Conference on Computer Vision, pages 554-559, Vancouver, Canada, July 2001.

    Google Scholar 

  141. Y. Li, S. Gong, and H. Liddell. Support vector regression and classification based multiview face detection and recognition. In Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition (FGR’00), Grenoble, France, pp.300–305, 2000.

    Google Scholar 

  142. A.J. Howell and H. Buxton. Towards unconstrained face recognition from image sequences. In Proceeding. of the IEEE International Conference on Automatic Face and Gesture Recognition (FGR’96), Killington, VT, pp. 224–229, 1996.

    Google Scholar 

  143. F. Roli and J. Kittler, (Eds.) Multiple Classifier Systems. Springer Verlag, Berlin, LNCS 2364, 2002.

    MATH  Google Scholar 

  144. B. Achermann and H. Bunke. Combination of classifiers on the decision level for face recognition. Technical Report IAM-96-002, Institut für Informatik und angewandte Mathematik, Universität Bern, January 1996.

    Google Scholar 

  145. X. Liu, and T. Chen. Video-based face recognition using adaptive hidden Markov models. In Proceedings CVPR03 (I), pp. 340–345.

    Google Scholar 

  146. Bicego, M., Grosso, E. and Tistarelli, M.: Person authentication from video of faces: a behavioral and physiological approach using Pseudo Hierarchical Hidden Markov Models. In Proceedings Intern.l Conference on Biometric Authentication 2006, Hong Kong, China, January 2006, pp 113-120, LNCS 3832.

    Google Scholar 

  147. Tistarelli, M., Bicego, M. and Grosso, E.: Dynamic face recognition: From human to machine vision. Image and Vision Computing: Special issue on Multimodal Biometrics, M. Tistarelli and J. Bigun ed.s, doi:10.1016/j.imavis.2007.05.006.

    Google Scholar 

  148. Arandjelovic, O., Cipolla, R.: Face Recognition from Face Motion Manifolds using Robust Kernel Resistor-Average Distance. In Proceedings FaceVideo04, pp 88.

    Google Scholar 

  149. Lee, K.C., Ho, J., Yang, M.H., Kriegman, D.J.: Video-based face recognition using probabilistic appearance manifolds. In Proceedings CVPR03 (I), pp 313-320.

    Google Scholar 

  150. Yamaguchi, O., Fukui, K., Maeda, K.: Face Recognition Using Temporal Image Sequence. In Proceedings IEEE AFGR98, pp 318-323.

    Google Scholar 

  151. K. Fukui and O. Yamaguchi: Face recognition using multiviewpoint patterns for robot vision. In Proceedings International Symposium of Robotics Research, 2003.

    Google Scholar 

  152. Shakhnarovich, G., Fisher, J.W., Darrell, T.J.: Face Recognition from Long-Term Observations. In Proceedings ECCV02 (III), pp 851.

    Google Scholar 

  153. Raytchev, B., Murase, H.: Unsupervised Face Recognition from Image Sequences. In Proceedings ICIP01(I), pp 1042-1045.

    Google Scholar 

  154. D.G. Lowe.: Object recognition from local scale invariant features. Proc. of International Conference on Computer Vision, pp 1150-1157, 1999.

    Google Scholar 

  155. D.R. Kisku, A. Rattani, E. Grosso, M. Tistarelli. Face Identification by SIFT-based Complete Graph Topology. Proc. of 5th IEEE Workshop on Automatic Identification Advanced Technologies, pp 63-68. Alghero, Italy, June 2007.

    Google Scholar 

  156. A.J. O, Toole, P.J. Phillips, F. Jiang, J.J. Ayadd, N. Penard and H. Abdi.: Face recognition algorithms surpass humans matching faces across changed in illumination. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29:1642–1646, 2007.

    Article  Google Scholar 

  157. A.J. O, Toole, H. Abdi,F. Jiang,J.J. and P.J. Phillips.: Fusing face recognition algorithms and humans. IEEE Transactions on Systems, Man and Cybernetics, 37:1149-1155, 2007.

    Article  Google Scholar 

  158. P.J. Phillips, W.T. Scruggs, A.J. O, Toole, P.J. Flynn, K.W. Bowyer, C.L.; Schott, and M. Sharpe. FiRVT 2006 and ICE 2006 Large-Scale Experimental Results. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 Mar. 2009. IEEE Computer Society, <http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.59>

  159. M. Husken, B. Brauckmann, S. Gehlen, and C. von der Malsburg. Strategies and benefits of fusion of 2D and 3D face recognition. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05),3:174, 2005.

    Article  Google Scholar 

  160. C. Liu.: Capitalize on dimensionality increasing techniques for improving face recognition Grand Challenge performance. IEEE Trans. Patt. Anal. Mach. Intelli. 28: 725-737, 2006.

    Article  Google Scholar 

  161. C.M Xie, M. Savvides and V Kumar.: Kernel correlation filter based redundant class-dependence feature analysis (KCFA) on FRGC2.0 Data IEEE International Workshop Analysis & Modeling Faces & Gestures, 32–43, 2005.

    Google Scholar 

  162. V. Bruce, Z. Henderson, C. Newman, and A.M. Burton.: Matching identities of familiar and unfamiliar faces caught on CCTV images. J. Exp. Psychol.: Appl. 7:207–218, 2001.

    Article  Google Scholar 

  163. A.M. Burton, V. Bruce, and P.J.B. Hancock.: From pixels to people: a model of familiar face recognition. Cogn. Sci. 23: 1–31, 1999.

    Article  Google Scholar 

  164. A.M. Burton, S. Wilson, M. Cowan, and V. Bruce.: Face recognition in poor-quality video. Psychological Science, 10:243–248, 1999.

    Article  Google Scholar 

  165. H. Abdi, D. Valentine, B. Edelman, and A.J. O’Toole.: More about the difference between men and women: evidence from linear neural networks and the principal-component approach. Perception 24 : 539–562, 1995.

    Article  Google Scholar 

  166. E. H. Aylward et al.: Brain activation during face perception: evidence of a development change. J. Cogn. Neurosci. 17:308–319, 2005.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Texas, Dallas TX, USA

    Alice O’Toole

  2. Computer Vision Laboratory, University of Sassari, Italy

    Massimo Tistarelli

Authors
  1. Alice O’Toole
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Massimo Tistarelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alice O’Toole .

Editor information

Editors and Affiliations

  1. Universitá of Sassari, Dipto. Architettura e Pianificazione, Piazza Duomo, 6, 07041 Alghero SS, Italy

    Massimo Tistarelli (Prof.) (Prof.)

  2. Chinese Academy of Science Institute of Automation, Center Biometrics Research & Security, Room 1227, Zhongguancun 95, 100080 Beijing, China

    Stan Z. Li

  3. University of Maryland, Center for Automation Research, College Park, MD 20742-3275, USA

    Rama Chellappa

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag London Limited

About this chapter

Cite this chapter

O’Toole, A., Tistarelli, M. (2009). Face Recognition in Humans and Machines. In: Tistarelli, M., Li, S.Z., Chellappa, R. (eds) Handbook of Remote Biometrics. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84882-385-3_5

Download citation

  • DOI: https://doi.org/10.1007/978-1-84882-385-3_5

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84882-384-6

  • Online ISBN: 978-1-84882-385-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation