Abstract
Elastic Bunch Graph Matching (EBGM) is regarded as a successful method to perform recognition on 2D face images. It employs an indiscriminate, part-separated aggregation method to conclude the overall recognition from local recognition results on face parts. Supported by the experimental evidence from cognitive research, we consider the human face recognition is an aggregation process that combines the local recognitions together in a inter-dependent and collective manner rather than a indiscriminate and part-separated process. This paper presents a improved EBGM face recognition system with the use of fuzzy fusion techniques (fuzzy measure and fuzzy integral) as the collective aggregation method. Experimental result shows the EBGM with fuzzy fusion produces better recognition performance over the original EBGM.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience (1991)
Wiskott, L., Fellous, J.-M., Kruger, N., von der Malsburg, C.: Face Recognition by Elastic Bunch Graph Matching, Technical Report IR-INI96-08 (1996)
Phillips, P.J., Moon, H., Rauss, P., Rizvi, S.S.: The FERET Evaluation Methodology for Face-Recognition Algorithms. In: Proc. First Int’l Conf. Audio and Video-Based Biometric Person Authentication (1997)
Li, X.B., Liu, Z.Q., Leung, K.M.: Detection of Vehicles from Traffic Scenes Using Fuzzy Integral. Pattern Recognition
Tahani, H., Keller, J.M.: Information fusion in computer vision using the fuzzy integral Fuzzy Measure Theory (1992)
Sugeno, M.: Fuzzy Measure and Fuzzy Integrals: a Surey Fuzzy Automation and Decision Processes. North Holland, Amsterdam (1977)
Valentine, T.: Cognitive and computational aspects of face recognition: Explorations in face space. Routledge, London (1995)
Fleet, D.J., Jepson, A.D.: Computation of component image velocity from local phase information. International Journal of Computer Vision (1990)
Jones, J., Palmer, L.: An Evaluation of the two dimensional Gabor filter model of simple receptive fields in cat striate cortex. Journal of Neurophysiology (1987)
Brunelli, R., Poggio, T.: Face recognition: Features versus Templates. IEEE Transactions on Pattern Analysis and Machine Intelligence (1993)
Ahuja, N., Kriegman, D.: Face Recognition using kernel eigenfaces. In: Proc. IEEE Int. Conf. Imaging Processing (2000)
Wechsler, H.: Evolutionary pursuit and its application to face recognition IEEE Trans. Pattern Anal. Machine Intell (2000)
Low, B.K.: Face detection: A survey Comput. Vision Imaging Understanding (2001)
Sommer, G.: Wavelet network for face processing. J. Opt. Soc. Amer. A (2002)
Wu, J., Lu, S., Toh, H.L.: Face recognition with radial basis function (rbf) neural networks IEEE Trans. Neural Networks (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, J., Liu, ZQ. (2005). EBGM with Fuzzy Fusion on Face Recognition. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_52
Download citation
DOI: https://doi.org/10.1007/11589990_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30462-3
Online ISBN: 978-3-540-31652-7
eBook Packages: Computer ScienceComputer Science (R0)