Learning from humans: Computational modeling of face recognition

Network: Computation in Neural Systems

Volumn 16, Issue 4, 2005, Pages 401-418

  Wallraven, Christian ^a   Schwaninger, Adrian ^a   Bülthoff, Heinrich H ^a  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

Configural and component information; Face recognition; Local features

Indexed keywords

AREA UNDER THE CURVE; ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPARATIVE STUDY; COMPUTER SIMULATION; FACE; HUMAN; LEARNING; METHODOLOGY; PATTERN RECOGNITION; PHOTOSTIMULATION; PHYSIOLOGY; PSYCHOLOGICAL MODEL; PSYCHOPHYSICS; RECOGNITION;

AREA UNDER CURVE; COMPUTER SIMULATION; FACE; HUMANS; LEARNING; MODELS, PSYCHOLOGICAL; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, VISUAL; PHOTIC STIMULATION; PSYCHOPHYSICS; RECOGNITION (PSYCHOLOGY);

EID: 33646248423     PISSN: 0954898X     EISSN: 13616536     Source Type: Journal    
DOI: 10.1080/09548980500508844     Document Type: Article

References (37)

1. Bahrick HP, Bahrick PO, Wittlinger RP. 1975. Fifty years of memory for names and faces: A cross-sectional approach. J Exp Psychol - General 104:54-75.
2. Biederman I, Gerhardstein PC. 1993. Recognizing depth-rotated objects - Evidence and conditions for 3-dimensional viewpoint invariance. J Exp Psychol - Hum Perception Perform 19:1162-1182.
3. Biederman I, Kalocsai P. 1997. Neurocomputational bases of object and face recognition. Phil Trans Royal Soc London Ser B - Biol Sciences 352:1203-1219.
4. Blanz V, Romdhani S, Vetter T. 2002. Face identification across different poses and illuminations with a 3D morphable model. In Proc. 5th Int Conf on Automatic Face and Gesture Recognition. pp 202-207.
5. Brunelli R, Poggio T. 1993. Face recognition: Features versus templates. IEEE Trans Pattern Recog Machine Intelligence 15:1042-1062.
6. Burl MC, Weber M, Perona P. 1998. A probabilistic approach to object recognition using local photometry and global geometry. In Proc ECCV'98. pp 628-641.
7. Furl N, O'Toole AJ, Phillips PJ. 2002. Face recognition algorithms as models of the other race effect. Cognitive Science 96:1-19.
8. Goffaux V, Hault B, Michel C, Vuong QC, Rossion B. 2005. The respective role of low and high spatial frequencies in supporting configural and featural processing of faces. Perception 34:77-86.
9. Green DM, Swets JA. 1966. Signal detection theory and psychophysics. New York: Wiley.
10. Hjelmas E, Low BK. 2001. Face detection: A survey. Comp Vision Image Understanding 83: 236-274.
11. Leder H, Bruce V. 2000. When inverted faces are recognized: The role of configural information in face recognition. Quart J Exp Psychol 53A:513-536.
12. Lowe D. 2004. Distinctive image features from scale-invariant keypoints. Int J Comp Vision 60:91-110.
13. Marr D. 1982. Vision. San Francisco: Freeman Publishers.
14. Maurer D, Le Grand R, Mondloch C. 2002. The many faces of configural processing. Trends Cognitive Science 6:255-260.
15. Murray JE, Yong E, Rhodes G. 2000. Revisiting the perception of upside-down faces. Psychol Science 11:498-502.
16. O'Toole A, Edelman S, Bülthoff HH. 1998. Stimulus-specific effects in face recognition over changes in viewpoint. Vision Res 38:2351-2363.
17. Penev PS, Atick JJ. 1996. Local feature analysis: A general statistical theory for object representation. Neural Systems 7:477-500.
18. Pilu M. 1997. A direct method for stereo correspondence based on singular value decomposition, In Proc Comp Vision Pattern Recognition. pp. 261-266.
19. Rock I. 1973. Orientation and form. New York: Academic Press.
20. Rock I. 1988. On Thompson's inverted-face phenomenon (Research Note). Perception 17:815-817.
21. Schiele B, Crowley JL. 1996. Object recognition using multidimensional receptive field histograms. In Proc ECCV'96. pp. 610-619.
22. Schwaninger A, Mast F. 1999. Why is face recognition so orientation-sensitive? Psychophysical evidence for an integrative model. Perception (Suppl.) 28:116.
23. Schwaninger A, Lobmaier J, Collishaw SM. 2002. Role of featural and configural information in familiar and unfamiliar face recognition. Lecture Notes Comp Science 2525:643-650.
24. Schwaninger A, Carbon CC, Leder H. 2003. Expert face processing: Specialization and constraints. In: Schwarzer G, Leder H, editors. Development of face processing, Göttingen: Hogrefe.
25. Searcy JH, Bartlett JC. 1996. Inversion and processing of component and spatial-relational information of faces. J Exp Psychol: Human Perception Performance 22:904-915.
26. Sergent J. 1984. An investigation into component and configurational processes underlying face recognition. Brit J Psychol 75:221-242.
27. Tanaka JW, Farah M. 1993. Parts and wholes in face recognition. Quart J Exp Psychol 46:225-245.
28. Thompson P. 1980. Margaret Thatcher - A new illusion. Perception 9:483-484.
29. Troje NF, Bülthoff HH. 1996. Face recognition under varying poses: The role of texture and shape. Vision Res 36:1761-1771.
30. Turk M, Pentland A. 1991. Eigenfaces for recognition. J Cognitive Neuroscience 3:71-86.
31. Ullman S, Vidal-Naquet M, Sali E. 2002 Visual features of intermediate complexity and their use in classification. Nature Neuroscience 5:682-687.
32. Wallraven C, Bülthoff HH. 2001. Automatic acquisition of exemplar-based representations for recognition from image sequences. In Proc of CVPR 2001 - Workshop on Models vs. Exemplars.
33. Wallraven C, Schwaninger A, Schuhmacher S, Bülthoff HH. 2002. View-based recognition of faces in man and machine: Re-visiting inter-extra-ortho. Lecture Notes Comp Science 2525:651-660.
34. Wallraven C, Caputo B, Graf A. 2003. Recognition with local features: the Kernel Recipe. In Proc ICCV 2003. pp. 257-264.
35. Weyrauch B, Huang J, Heisele B, Blanz V. 2004. Component-based face recognition with 3D morphable models. First IEEE Workshop on Face Processing in Video.
36. Wiskott L, Fellous J, Krüger N, v. d. Malsburg C. 1997. Face recognition by elastic bunch graph matching. IEEE Trans Pattern Machine Intelligence 19:775-779.
37. Yin RK. 1969. Looking at upside-down faces. J Exp Psychol 81:141-145.