Abstract
A methodology for determining the level of confidence of a sub-region in the overall classification of a given face image affected due to varying expressions, illuminations and partial occlusions is presented in this paper. The technique for obtaining the weights for each individual region of the test image is based on a measure of optical flow between that test image and a face model. Individual image regions or the modules are also assigned additional weights by arranging them in the order of their importance in classification. The approach presented is applicable mainly in scenarios where the number of samples in the training set is too little. A K-nearest neighbor distance measure is used in classifying each module of the test image after dimensionality reduction. A total score is calculated for each training class based on the classification result of each module and its associated weights. Considerable increase in recognition accuracy has been observed for PCA, LDA and ICA based linear subspace approaches when implemented using the proposed technique.
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References
Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal Cognitive Neuroscience (1991)
Pentland, A., Moghaddam, B., Starner, T.: View-based and modular eigenspaces for face recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 84–91 (1994)
Zhao, W., Krishnaswamy, A., Chellappa, R., Swets, D.L., Weng, J.: Discriminant Analysis of Principal Components for Face Recognition. In: Int. Conference on Automatic Face and Gesture Recognition, vol. 3, pp. 336–341 (1998)
Huang, J., Yuen, P., Chen, C., Sheng, W., Lai, J.H.: Kernel subspace LDA with optimized kernel parameters on face recognition. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp. 327–332 (2004)
Martinez, A.: Recognition of partially occluded and/or imprecisely localized faces using a probabilistic approach. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 712–717 (2000)
Zhang, Y., Martinez, A.M.: Recognition of expression variant faces using weighted subspaces. In: Proceedings of the 17th International Conference on Pattern Recognition, pp. 149–152 (2004)
Tan, K., Chen, S.: Adaptively weighted sub-pattern PCA for face recognition. Neurocomputing 64, 505–511 (2005)
Lucas, K.: An iterative image registration technique with an application to stereo vision. In: Proc. DARPA Image Understanding Workshop, pp. 121–130 (1981)
Moghaddam, B., Pentland, A.: Probabilistic visual learning of object representation. IEEE transaction on Pattern Analysis and Machine Intelligence, 696–710 (1997)
Lam, K.M., Siu, W.C., Yang, S.: Human face recognition based on spatially weighted Hausdorff distance. Pattern Recognition Letters, 499–507 (2003)
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Gundimada, S., Asari, V. (2006). An Adaptive Weight Assignment Scheme in Linear Subspace Approaches for Face Recognition. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3852. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612704_54
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DOI: https://doi.org/10.1007/11612704_54
Publisher Name: Springer, Berlin, Heidelberg
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