Learning identity with radial basis function networks

Neurocomputing

Volumn 20, Issue 1-3, 1998, Pages 15-34

  Howell, A Jonathan ^a   Buxton, Hilary ^a  

^a UNIVERSITY OF SUSSEX   (United Kingdom)

Author keywords

Face recognition; Image sequences; Invariance; Receptive field functions; Time delay networks

Indexed keywords

COMPUTER VISION; IMAGE ANALYSIS; IMAGE QUALITY; LEARNING SYSTEMS; OBJECT RECOGNITION; WAVELET TRANSFORMS;

DIFFERENCE OF GAUSSIAN (DOG) FILTERING; IMAGE SEQUENCE ANALYSIS; RADIAL BASIS FUNCTION (RBF) NETWORKS; RECEPTIVE FIELD FUNCTIONS; TIME DELAY (TD) NETWORKS;

NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; FACE; IMAGE ANALYSIS; PRIORITY JOURNAL; RECOGNITION; SIGNAL TRANSDUCTION; VIDEORECORDING;

EID: 0032584915     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0925-2312(98)00016-2     Document Type: Article

References (43)

1. S. Ahmad, V. Tresp, Some solutions to the missing feature problem in vision, in: S.J. Hanson, J.D. Cowan, C.L. Giles (Eds.), Advances in Neural Information Processing Systems, vol. 5, Morgan Kaufmann, San Mateo, CA, 1993, pp. 393-400.
2. Baron R.J. Mechanisms of human facial recognition. Int. J. Man-Mach. Stud. 15:1981;137-178.
3. M.R. Berthold, A time delay radial basis function network for phoneme recognition, Proc. IEEE Int. Conf. Neural Networks, vol. 7, IEEE Computer Society Press, Orlando, FL, 1994, pp. 4470-4473.
4. D.J. Beymer, Face recognition under varying pose, Proc. IEEE Conf. on Computer Vision & Pattern Recognition, IEEE Computer Society Press, Seattle, WA, 1994, pp. 756-761.
5. D.J. Beymer, T. Poggio, Face recognition from one example view, Proc. Int. Conf. on Computer Vision, IEEE Computer Society Press, Cambridge, MA, 1995, pp. 500-507.
6. Beymer D.J., Poggio T. Image representations for visual learning. Science. 272:1996;1905-1909.
7. Bishop C.M. Neural Networks for Pattern Recognition. 1995;Oxford University Press, Oxford, UK.
8. A.F. Bobick, Computers seeing action, in: R.B. Fisher, E. Trucco (Eds.), Proc. British Machine Vision Conf., BMVA Press, Edinburgh, 1996, pp. 13-22.
9. Bruce V. Recognising Faces. 1988;Lawrence Erlbaum Associates, London.
10. Bruce V., Young A. Understanding face recognition. British J. Psychol. 77:1986;305-327.
11. R. Brunelli, T. Poggio, HyperBF networks for real object recognition, in: J. Myopoulos, R. Reiter (Eds.), Proc. Int. Joint Conf. on Artificial Intelligence, Morgan Kaufmann, Sydney, Australia, 1991, pp. 1278-1284.
12. Brunelli R., Poggio T. Face recognition: features versus templates. IEEE Trans. Pattern Anal. Mach. Intell. 15:1993;1042-1052.
13. H. Buxton, S. Gong, Advanced visual surveillance using Bayesian nets, in: J.L. Mundy, T. Strat (Eds.), Proc. IEEE Workshop on Context-Based Vision, Int. Conf. on Computer Vision, IEEE Computer Society Press, Cambridge, MA, 1995.
14. S. Edelman, D. Reisfeld, Y. Yeshurun, Learning to recognize faces from examples, in: G. Sandini (Ed.), Proc. European Conf. on Computer Vision, Lecture Notes in Computer Science, vol. 588, Springer, Santa Margherita Ligure, Italy, 1992, pp. 787-791.
15. Girosi F. Some extensions of radial basis functions and their applications in artificial intelligence. Comput. Math. Appl. 24(12):1992;61-80.
16. Hertz J.A., Krogh A., Palmer R.G. Introduction to the Theory of Neural Computation. 1991;Addison-Wesley, Redwood City, CA.
17. A.J. Howell, H. Buxton, Receptive field functions for face recognition, Proc. 2nd Int. Workshop on Parallel Modelling of Neural Operators for Pattern Recognition, University of Algarve, Faro, Portugal, 1995, pp. 83-92.
18. A.J. Howell, H. Buxton, Face recognition using radial basis function neural networks, in: R.B. Fisher, E. Trucco (Eds.), Proc. British Machine Vision Conf., BMVA Press, Edinburgh, 1996, pp. 455-464.
19. Kirby M., Sirovich L. Application of the Karhunen-Loève procedure for the characterization of human faces. IEEE Trans. Pattern Anal. Mach. Intell. 12:1990;103-108.
20. Lawrence S., Giles C.L., Tsoi A.C., Back A.D. Face recognition: a convolutional neural network approach. IEEE Trans. Neural Networks. 8:1997;98-113.
21. Lin S.-H., Kung S.-Y., Lin L.-J. Face recognition/detection by probabilistic decision-based neural network. IEEE Trans. Neural Networks. 8:1997;114-132.
22. Logothetis N.K., Pauls J., Bülthoff H.H., Poggio T. View-dependent object recognition by monkeys. Current Biol. 4:1994;401-414.
23. S.M. Lucas, Face recognition with the continuous n-tuple classifier, in: A.F. Clark (Ed.), Proc. British Machine Vision Conf., BMVA Press, Colchester, UK, 1997, pp. 222-231.
24. Marr D., Hildreth E. Theory of edge detection. Proc. Roy. Soc. London Ser. B. 207:1980;187-217.
25. S. McKenna, S. Gong, J.J. Collins, Face tracking and pose representation, in: R.B. Fisher, E. Trucco (Eds.), Proc. British Machine Vision Conf., BMVA Press, Edinburgh, 1996, pp. 755-764
26. J. Moody, C. Darken, Learning with localized receptive fields, in: D. Touretzky, G. Hinton, T. Sejnowski (Eds.), Proc. 1988 Connectionist Models Summer School, Morgan Kaufmann, Pittsburgh, PA, 1988, pp. 133-143.
27. Moody J., Darken C. Fast learning in networks of locally-tuned processing units. Neural Comput. 1:1989;281-294.
28. Y. Moses, Y. Adini, S. Ullman, Face recognition: the problem of compensating for illumination changes, in: J.O. Eklundh (Ed.), Proc. European Conf. on Computer Vision, Lecture Notes in Computer Science, vol. 800, Springer, Stockholm, Sweden, 1994, pp. 286-296.
29. M.C. Mozer, Neural net architectures for temporal sequence processing, in: A.S. Weigend, N.A. Gershenfeld (Eds.), Time Series Prediction: Predicting the Future and Understanding the Past, Addison-Wesley, Redwood City, CA, 1994, pp. 243-264.
30. Musavi M.T., Ahmad W., Chan K.H., Faris K.B., Hummels D.M. On the training of radial basis function classifiers. Neural Networks. 5:1992;595-603.
31. S. Nagaya, S. Seki, R. Oka, A theoretical consideration of pattern space trajectory for gesture spotting recognition, Proc. Int. Conf. on Automatic Face & Gesture Recognition, IEEE Computer Society Press, Killington, VT, 1996, pp. 72-77.
32. Pentland A. Smart rooms. Sci. Amer. 274(4):1996;68-76.
33. A. Pentland, B. Moghaddam, T. Starner, View-based and modular eigenspaces for face recognition, Proc. IEEE Conf. on Computer Vision & Pattern Recognition, IEEE Computer Society Press, Seattle, WA, 1994, pp. 84-91.
34. Perrett D.I., Mistlin A.J., Chitty A.J. Visual neurons responsive to faces. Trends Neurosci. 10:1989;358-364.
35. Perrett D.I., Oram M.W. Neurophysiology of shape processing. Image Vision Comput. 11:1993;317-333.
36. Poggio T., Edelman S. A network that learns to recognize three-dimensional objects. Nature. 343:1990;263-266.
37. Poggio T., Girosi F. Networks for approximation and learning. Proc. IEEE. 78:1990;1481-1497.
38. Poggio T., Girosi F. Regularization algorithms for learning that are equivalent to multilayer networks. Science. 247:1990;978-982.
39. A. Psarrou, H. Buxton, Motion analysis with recurrent neural nets, Proc. Int. Conf. on Artificial Neural Networks, Springer, Sorrento, Italy, 1994, pp. 54-57.
40. F.S. Samaria, Face Recognition using Hidden Markov Models, Ph.D. Thesis, Cambridge University, Cambridge, UK, 1994.
41. F.S. Samaria, A.C. Harter, Parameterisation of a stochastic model for human face identification, Proc. 2nd IEEE Workshop on Applications of Computer Vision, Sarasota, FL, 1994.
42. T.J. Stonham, Practical face recognition and verification with WISARD, in: H.D. Ellis, M.A. Jeeves, F. Newcombe, A.W. Young (Eds.), Aspects of Face Processing, Martinus Nijhoff, Dordrecht, 1986.
43. Turk M., Pentland A. Eigenfaces for recognition. J. Cognitive Neurosci. 3:1991;71-86.