Abstract
The utility of face recognition for multimedia indexing is enhanced by using accurate detection and alignment of salient invariant face features. The face recognition can be performed using template matching or a feature-based-approach, but both these methods suffer from occlusion and require an a priori model for extracting information. To avoid these drawbacks, we present in this paper a complete scheme for face recognition based on salient feature extraction in challenging conditions, which is performed without an a priori or learned model. These features are used in a matching process that overcomes occlusion effects and facial expressions using the dynamic space warping which aligns each feature in the query image, if possible, with its corresponding feature in the gallery set. Thus, we make face recognition robust to low frequency variations (like the presence of occlusion, etc) as well as to high frequency variations (like expression, gender, etc). A maximum likelihood scheme is used to make the recognition process more precise, as is shown in the experiments.
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References
M. Turk and A. Pentland: Eigenfaces for Recognition. Journal of Cognitive Neuroscience. Vol. 3, Num. 1. (1991).
Ballard, D. H.: Generalizing the Hough Transform to Detect Arbitrary Shapes. Pattern Recognition. Vol. 13, Num. 2. (1981).
Ballard, Dana H. and Christopher M. Brown. Computer Vision. Prentice Hall-Inc. Englewood Cliffs-New Jersey. (1982).
R. Bolles and R. Cain: Recognizing and localizing partially visible objects: The local-features-focus method. International Journal of Robotics Research. Vol. 1, Num. 3. (1982) 57–82.
H. Ney: The Use of a One-Stage Dynamic Programming Algorithm for Connected Word Recognition. IEEE Trans. ASSP. Vol. 32, Num. 2. (1984).
R. Brunelli and T. Poggio: Face recognition: Features versus templates. IEEE Trans. Pattern Anal. and Machine Intelligence. Vol. 15, Num. 10. (1993).
C. Nastar and A. Pentland: Matching and recognition using deformable intensity surfaces. In Proc. IEEE Sym. on Vision. (1995).
S. Gilles: Robust Description and Matching of Images. PhD thesis, Oxford University. (1998).
S. Sclaroff and A. Pentland: Modal matching for correspondence and recognition. IEEE Trans. Pattern Anal. and Machine Intelligence. Vol. 17, Num. 6. (1995) 545–561.
P. Viola: Complex feature recognition: A bayesian approach for learning to recognize objects. A.I. Memo. No. 1591, MIT. (1996).
W. Grimson and T. Lonzano-Perez: Model-Based Recognition and Localisation from Sparse Range or Tactile Data. International Journal of Robotics Research. Vol. 3, Num. 3. (1984) 3–35.
C. Nastar and C. Meilhac: Real-Time Face Recognition Using Feature Combination. IEEE International Conference on Face and Gesture Recognition. (1998).
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© 2002 Springer-Verlag Berlin Heidelberg
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Sahbi, H., Boujemaa, N. (2002). Robust Face Recognition Using Dynamic Space Warping. In: Tistarelli, M., Bigun, J., Jain, A.K. (eds) Biometric Authentication. BioAW 2002. Lecture Notes in Computer Science, vol 2359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47917-1_13
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DOI: https://doi.org/10.1007/3-540-47917-1_13
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