Using biologically inspired features for face processing

International Journal of Computer Vision

Volumn 76, Issue 1, 2008, Pages 93-104

  Meyers, Ethan ^a   Wolf, Lior ^b  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b TEL AVIV UNIVERSITY   (Israel)

Author keywords

Biologically motivated computer vision; Face identification; Face recognition; Kernel methods; Learning distance measures

Indexed keywords

COMPUTER VISION; ELECTROPHYSIOLOGY; FEATURE EXTRACTION; LEARNING SYSTEMS; MAGNETIC RESONANCE IMAGING; MATHEMATICAL MODELS;

BIOLOGICALLY MOTIVATED COMPUTER VISION; FACE IDENTIFICATION; KERNEL METHODS; LEARNING DISTANCE MEASURES; POGGIO MODELS;

FACE RECOGNITION;

EID: 36849063074     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-007-0058-8     Document Type: Article

References (31)

1. Ahonen, T., Hadid, A., & Pietikainen, M. (2004). Face recognition with local binary patterns. In 8th European conference on computer vision (pp. 469-481).
2. Bar-Hillel, A., Hertz, T., Shental, N., & Weinshall, D. (2005). Learning a mahalanobis metric from equivalence constraints. Journal of Machine Learning Research, 6, 937-965.
3. Bieschi, S., & Wolf, L. (2005). A unified system for object detection, texture recognition and cotext analysis based on the standard model feature set. In Proceedings of the British machine vision conference.
4. Bolme, D. S., Beveridge, J. R., Teixeira, M., & Draper, B. A. (2003). The CSU face identification evaluation system: its purpose, features and structure. In Conference on vision systems (pp. 304-311).
5. Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In Conference on computer vision and pattern recognition (pp. 886-893).
6. Etemad, K., & Chellappa, R. (1997). Discriminant analysis for recognition of human face images. Journal of Optical Society of America, 14, 1724-1733.
7. Fei-Fei, L., Fergus, R., & Perona, P. (2004). Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In CVPR, workshop on generative-model based vision.
8. Fergus, R., Perona, P., & Zisserman, A. (2003). Object class recognition by unsupervised scale-invariant learning. In Conference on computer vision and pattern recognition (Vol. 2, pp. 264-271).
9. Fukushima, K. (1980). Neocognitron: a self organizing neural network model for a mechanism for pattern recognition unaffected by shift in position. Biological Cybernetics, 36(4), 193-202.
10. Goldberger, J., Roweis, S., Hinton, G., & Salakhutdinov, R. (2004). Neighbourhood component analysis. Neural Information Processing Systems, 17, 513-520.
11. Hastie, T., & Tibshirani, R. (1996). Discriminant adaptive nearest neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(6), 607-616.
12. Jones, M., & Viola, P. (2003). Face recognition using boosted local features. In Proceedings of international conference on computer vision.
13. Kanwisher, N., McDermott, J., & Chun, M. M. (1997). The fusiform face area: a module in human extrastriate cortex specialized for face perception. Journal of Neuroscience, 17(11), 4302-4311.
14. Lowe, D. G. (2003). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91-110.
15. Martinez, A. M., & Benavente, R. (1998). The AR face database. CVC Technical Report #24.
16. Moghaddam, B., Nastar, C., & Pentland, A. (1996). A Bayesian similarity measure for direct image matching. In Conference on computer vision and pattern recognition (p. 638).
17. Mutch, J., & Lowe, D. (2006). Multiclass object recognition using sparse, localized features. In Conference on computer vision and pattern recognition (pp. 11-18).
18. Ojala, T., Pietikainen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on feature distributions. Pattern Recognition, 29, 51-59.
19. Olshausen, B. A., & Field, D. J. (1997). Sparse coding with an overcomplete basis set: a strategy employed by V1? Vision Research, 37, 3311-3325.
20. Phillips, P. J., Moon, H., Rizvi, S. A., & Rauss, P. J. (2002). The FERET evaluation methodology for face recognition algorithms. Pattern Analysis and Machine Intelligence, 22(10), 1090-1104.
21. Pontil, M., & Verri, A. (1998). Support vector machines for 3D object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(6), 637-646.
22. Riesenhuber, M., & Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience, 2(11), 1019-1025.
23. Samaria, F., & Harter, A. (1994). Parameterisation of a stochastic model for human face identification. In 2nd IEEE workshop on applications of computer vision.
24. Schölkopf, B., & Smola, A. J. (2002). Learning with kernels. Cambridge: MIT.
25. Serre, T., Wolf, L., & Poggio, T. (2005). Object recognition with features inspired by visual cortex. In Conference on computer vision and pattern recognition (Vol. 2, pp. 994-1000).
26. Shan, C., Gong, S., & McOwan, P. (2005). Conditional mutualinformation based boosting for facial expression recognition. In Proceedings of the British machine vision conference.
27. Turk, M., & Pentlanbd, A. (1991). Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3, 71-86.
28. Ungerleider, L. G., & Mishkin, M. (1982) Two cortical visual systems. In: Analysis of visual behavior, (pp. 549-586). Cambridge: MIT.
29. Wang, H., Li, S., & Wang, Y. (2004). Face recognition under varying lighting conditions using self quotient image. In IEEE international conference on automatic face and gesture recognition (pp. 819-824).
30. Weber, M., Welling, M., & Perona, P. (2000). Unsupervised learning of models for recognition. In European conference on computer vision (pp. 19-32).
31. Zigmond, M. J. (1999). Fundamental neuroscience (1st ed.). New York: Academic Press.