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Recognition of Human Faces: From Biological to Artificial Vision

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Advances in Brain, Vision, and Artificial Intelligence (BVAI 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4729))

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Abstract

Face recognition is among the most challenging techniques for personal identity verification. Even though it is so natural for humans, there are still many hidden mechanisms which are still to be discovered. According to the most recent neurophysiological studies, the use of dynamic information is extremely important for humans in visual perception of biological forms and motion. Moreover, motion processing is also involved in the selection of the most informative areas of the face and consequently directing the attention. This paper provides an overview and some new insights on the use of dynamic visual information for face recognition, both for exploiting the temporal information and to define the most relevant areas to be analyzed on the face. In this context, both physical and behavioral features emerge in the face representation.

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References

  1. Knight, B., Johnston, A.: The role of movement in face recognition. Visual Cognition 4, 265–274 (1997)

    Article  Google Scholar 

  2. Yamaguchi, O., Fukui, K., Maeda, K.: Face recognition using temporal image sequence. In: Proc. Int. Conf. on Automatic Face and Gesture Recognition (1998)

    Google Scholar 

  3. Biuk, Z., Loncaric, S.: Face recognition from multi-pose image sequence. In: Proc. of Int. Symp. on Image and Signal Processing and Analysis (2001)

    Google Scholar 

  4. Li, Y.: Dynamic face models: construction and applications. PhD thesis, Queen Mary, University of London (2001)

    Google Scholar 

  5. Shakhnarovich, G., Fisher, J.W., Darrell, T.: Face recognition from long-term observations. In: Proc. of European Conf. on Computer Vision (2002)

    Google Scholar 

  6. Zhou, S., Krueger, V., Chellappa, R.: Probabilistic recognition of human faces from video. Computer Vision and Image Understanding 91, 214–245 (2003)

    Article  Google Scholar 

  7. Liu, X., Chen, T.: Video-based face recognition using adaptive hidden markov models. In: Proc. Int. Conf. on Computer Vision and Pattern Recognition (2003)

    Google Scholar 

  8. Lee, K.C., Ho, J., Yang, M.H., Kriegman, D.: Video-based face recognition using probabilistic appearance manifolds. In: Proc. Int. Conf. on Computer Vision and Pattern Recognition (2003)

    Google Scholar 

  9. Hadid, A., Pietikäinen, M.: An experimental investigation about the integration of facial dynamics in video-based face recognition. Electronic Letters on Computer Vision and Image Analysis 5(1), 1–13 (2005)

    Google Scholar 

  10. Vaina, L.M., Solomon, J., Chowdhury, S., Sinha, P., Belliveau, J.W.: Functional Neuroanatomy of Biological Motion Perception in Humans. Proc. of the National Academy of Sciences of the United States of America 98(20), 11656–11661 (2001)

    Article  Google Scholar 

  11. OToole, A.J., Roark, D.A., Abdi, H.: Recognizing moving faces: A psychological and neural synthesis. Trends in Cognitive Science 6, 261–266 (2002)

    Article  Google Scholar 

  12. Darwin, C.: The expression of the emotions in man and animals. John Murray, London, UK (1965) (original work published 1872)

    Google Scholar 

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

    Google Scholar 

  14. Walton, G.E., Bower, T.G.R.: Newborns form “prototypes” in less than 1 minute. Psychological Science 4, 203–205 (1993)

    Article  Google Scholar 

  15. Fagan, J.: Infants’ recognition memory for face. Journal of Experimental Child Psychology 14, 453–476 (1972)

    Article  Google Scholar 

  16. de Haan, M., Nelson, C.A.: Recognition of the mother’s face by 6-month-old infants: A neurobehavioral study. Child Development 68, 187–210 (1997)

    Article  Google Scholar 

  17. Ballard, D.H.: Animate vision. Artificial Intelligence 48, 57–86 (1991)

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  21. Curcio, C.A., Sloan, K.R., Kalina, R.E., Hendrickson, A.E.: Human photoreceptor topography. Journal of Computational Neurology 292(4), 497–523 (1990)

    Article  Google Scholar 

  22. Sandini, G., Metta, G.: Retina- like sensors: motivations, technology and applications. In: Secomb, T.W., Barth, F., Humphrey, P. (eds.) Sensors and Sensing in Biology and Engineering, Springer, Heidelberg (2002)

    Google Scholar 

  23. Burt, P.J.: Smart sensing in machine vision. In: Machine Vision: Algorithms, Architectures, and Systems, Academic Press, London (1988)

    Google Scholar 

  24. Tong, F., Li, Z.N.: The reciprocal-wedge transform for space-variant sensing. In: 4th IEEE Intl. Conference on Computer Vision, Berlin, pp. 330–334. IEEE Computer Society Press, Los Alamitos (1993)

    Chapter  Google Scholar 

  25. Schwartz, E.L.: Spatial mapping in the primate sensory projection: Analytic structure and relevance to perception. Biological Cybernetics 25, 181–194 (1977)

    Article  Google Scholar 

  26. Fisher, T.E., Juday, R.D.: A programmable video image remapper. In: Proceedings of SPIE, vol. 938, pp. 122–128 (1988)

    Google Scholar 

  27. Grosso, E., Tistarelli, M.: Log-polar Stereo for Anthropomorphic Robots. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 299–313. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  28. Yarbus, A.L.: Eye Movements and Vision. Plenum Press, New York (1967)

    Google Scholar 

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

    Google Scholar 

  30. Shepherd, J.: Social factors in face recognition. In: Davies, G., Ellis, H., Shepherd, J. (eds.) Perceiving and remembering face, pp. 55–79. Academic Press, London (1981)

    Google Scholar 

  31. Nahm, F.K.D., Perret, A., Amaral, D., Albright, T.D.: How do monkeys look at faces? Journal of Cognitive Neuroscience 9, 611–623 (1997)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  34. Tistarelli, M., Grosso, E.: Active vision-based face authentication. Image and Vision Computing: Special issue on Facial Image Analysis 18(4), 299–314 (2000)

    Google Scholar 

  35. Bicego, M., Grosso, E., Tistarelli, M.: On finding differences between faces. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 329–338. Springer, Heidelberg (2005)

    Google Scholar 

  36. Wiskott, L., Fellous, J.M., der Malsburg, C.V.: Face recognition by elastic bunch graph matching. IEEE Trans. on Pattern Analysis and Machine Intelligence 19, 775–779 (1997)

    Article  Google Scholar 

  37. Tsotsos, J., Culhane, S., Wai, W., Lai, Y., Davis, N., Nuflo, F.: Modelling visual attention via selective tuning. Artificial Intelligence 78, 507–545 (1995)

    Article  Google Scholar 

  38. Lindeberg, T.: Detecting salient blob-like image structures and their scales with a scale-space primal sketch: A method for focus-of-attention. Int. Journal of Computer Vision 11(3), 283–318 (1993)

    Article  Google Scholar 

  39. Koch, C., Ullman, S.: Shifts in selective visual-attention - towards the underlying neural circuitry. Human Neurobiology 4, 219–227 (1985)

    Google Scholar 

  40. Salah, A., Alpaydın, E., Akarun, L.: A selective attention-based method for visual pattern recognition with application to handwritten digit recognition and face recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(3), 420–425 (2002)

    Article  Google Scholar 

  41. González-Jiménez, D., Alba-Castro, J.: Biometrics discriminative face recognition through Gabor responses and sketch distortion. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3523, pp. 513–520. Springer, Heidelberg (2005)

    Google Scholar 

  42. Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. Journal of Computer Vision 60(2), 91–110 (2004)

    Article  Google Scholar 

  43. Penev, P., Atick, J.: Local feature analysis: a general statistical theory for object representation. Network: computation in Neural Systems 7(3), 477–500 (1996)

    Article  MATH  Google Scholar 

  44. Li, S., Hou, X., Zhang, H.: Learning spatially localized, parts-based representation. Computer Vision and Image Understanding 1, 207–212 (2001)

    Google Scholar 

  45. Kim, J., Choi, J., Yi, J., Turk, M.: Effective representation using ica for face recognition robust to local distortion and partial occlusion. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(12), 1977–1981 (2005)

    Article  Google Scholar 

  46. Ullman, S., Vidal-Naquet, M., Sali, E.: Visual features of intermediate complexity and their use in classification. Nature Neuroscience 5, 682–687 (2002)

    Google Scholar 

  47. Agarwal, S., Roth, D.: Learning a sparse representation for object detection. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 113–130. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  48. Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: Proc. Int. Conf. on Computer Vision and Pattern Recognition, vol. 2, p. 264 (2003)

    Google Scholar 

  49. Dorko, G., Schmid, C.: Selection of scale-invariant parts for object class recognition. In: Proc. Int. Conf. on Computer Vision, vol. 2, pp. 634–640 (2003)

    Google Scholar 

  50. Csurka, G., Dance, C., Bray, C., Fan, L., Willamowski, J.: Visual categorization with bags of keypoints. In: Proc. Workshop Pattern Recognition and Machine Learning in Computer Vision (2004)

    Google Scholar 

  51. Jojic, N., Frey, B., Kannan, A.: Epitomic analysis of appearance and shape. In: Proc. Int. Conf. on Computer Vision, vol. 2, pp. 34–41 (2003)

    Google Scholar 

  52. Haxby, J.V., Hoffman, E.A., Gobbini, M.I.: The distributed human neural system for face perception. Trends in Cognitive Sciences 4(6), 223–233 (2000)

    Article  Google Scholar 

  53. Wiskott, L., Fellous, J.M., Kruger, N., von der Malsburg, C.: Face recognition and gender determination. In: Proceedings Int.l Workshop on Automatic Face and Gesture Recognition, Zurich, Switzerland, pp. 92–97 (1995)

    Google Scholar 

  54. Wechsler, H., Phillips, P., Bruce, V., Soulie, F., Huang, T. (eds.): Face Recognition. From Theory to Applications. NATO ASI Series F, vol. 163. Springer, Heidelberg

    Google Scholar 

  55. Cottrell, G., Metcalfe, J.: Face, gender and emotion recognition using holons. In: Touretzky, D. (ed.) Advances in Neural Information Processing Systems, San Mateo, CA, vol. 3, pp. 564–571. Morgan Kaufmann, San Francisco (1991)

    Google Scholar 

  56. Braathen, B., Bartlett, M.S., Littlewort, G., Movellan, J.R.: First Steps Towards Automatic Recognition of Spontaneous Facial Action Units. In: ACM Workshop on Perceptive User Interfaces, Orlando, FL, November 15-16, 2001, ACM Press, New York (2001)

    Google Scholar 

  57. Picard, R.W.: Toward computers that recognize and respond to user emotion. IBM System (39), 3/4 (2000)

    Google Scholar 

  58. Picard, R.W.: Building HAL: Computers that sense, recognize, and respond to human emotion. MIT Media-Lab TR-532, also in Society of Photo-Optical Instrumentation Engineers. Human Vision and Electronic Imaging VI, part of SPIE9s Photonics West (2001)

    Google Scholar 

  59. Bicego, M., Grosso, E., Tistarelli, M.: Person authentication from video of faces: a behavioral and physiological approach using Pseudo Hierarchical Hidden Markov Models. In: Zhang, D., Jain, A.K. (eds.) Advances in Biometrics. LNCS, vol. 3832, pp. 113–120. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  60. Rabiner, L.: A tutorial on Hidden Markov Models and selected applications in speech recognition. Proc. of IEEE 77(2), 257–286 (1989)

    Article  Google Scholar 

  61. Kohir, V.V., Desai, U.B.: Face recognition using DCT-HMM approach. In: AFIART. Proc. Workshop on Advances in Facial Image Analysis and Recogniti Technology, Freiburg, Germany (1998)

    Google Scholar 

  62. Samaria, F.: Face recognition using Hidden Markov Models. PhD thesis, Engineering Department, Cambridge University (October 1994)

    Google Scholar 

  63. Nefian, A.V., Hayes, M.H.: Hidden Markov models for face recognition. In: ICASSP. Proc. Int. Conf. on Acoustics, Speech and Signal Processing, Seattle, pp. 2721–2724 (1998)

    Google Scholar 

  64. Bicego, M., Castellani, U., Murino, V.: Using Hidden Markov Models and wavelets for face recognition. In: IEEE. Proc. of Int. Conf on Image Analysis and Processing, pp. 52–56. IEEE Computer Society Press, Los Alamitos (2003)

    Google Scholar 

  65. Bicego, M., Grosso, E., Tistarelli, M.: Probabilistic face authentication using hidden markov models. In: Proc. of SPIE Int. Workshop on Biometric Technology for Human Identification (2005)

    Google Scholar 

  66. Schwarz, G.: Estimating the dimension of a model. The Annals of Statistics 6(2), 461–464 (1978)

    MATH  MathSciNet  Google Scholar 

  67. Fine, S., Singer, Y., Tishby, N.: The hierarchical hidden markov model: Analysis and applications. Machine Learning 32, 41–62 (1998)

    Article  MATH  Google Scholar 

  68. Smyth, P.: Clustering sequences with hidden Markov models. In: Mozer, M., Jordan, M., Petsche, T. (eds.) Advances in Neural Information Processing Systems, vol. 9, p. 648. MIT Press, Cambridge (1997)

    Google Scholar 

  69. Panuccio, A., Bicego, M., Murino, V.: A Hidden Markov model-based approach to sequential data clustering. In: Caelli, T.M., Amin, A., Duin, R.P.W., Kamel, M.S., de Ridder, D. (eds.) SPR 2002 and SSPR 2002. LNCS, vol. 2396, pp. 734–742. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  70. Rabiner, L., Lee, C., Juang, B., Wilpon, J.: HMM clustering for connected word recognition. In: ICASSP. Proc. Int. Conf. on Acoustics, Speech and Signal Processing, pp. 405–408 (1989)

    Google Scholar 

  71. Li, C.: A Bayesian Approach to Temporal Data Clustering using Hidden Markov Model Methodology. PhD thesis, Vanderbilt University (2000)

    Google Scholar 

  72. Jain, A.K., Dubes, R.: Algorithms for clustering data. Prentice-Hall, Englewood Cliffs (1988)

    MATH  Google Scholar 

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Authors and Affiliations

  1. DAP - University of Sassari, piazza Duomo 6 - 07041 Alghero (SS), Italy

    Massimo Tistarelli

  2. DSL - University of Sassari, piazza Università 21 - 07100 Sassari, Italy

    Linda Brodo

  3. DEIR - University of Sassari, via Torre Tonda 34 - 07100 Sassari, Italy

    Andrea Lagorio & Manuele Bicego

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  1. Massimo Tistarelli
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  2. Linda Brodo
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  3. Andrea Lagorio
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  4. Manuele Bicego
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Francesco Mele Giuliana Ramella Silvia Santillo Francesco Ventriglia

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Tistarelli, M., Brodo, L., Lagorio, A., Bicego, M. (2007). Recognition of Human Faces: From Biological to Artificial Vision. In: Mele, F., Ramella, G., Santillo, S., Ventriglia, F. (eds) Advances in Brain, Vision, and Artificial Intelligence. BVAI 2007. Lecture Notes in Computer Science, vol 4729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75555-5_19

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  • DOI: https://doi.org/10.1007/978-3-540-75555-5_19

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