Abstract
In this paper, a novel face recognition algorithm based on histogram of modular Gabor feature and support vector machines is proposed. In this method, each face image is separate into several parts on which Gabor transformation is performed, respectively and then employed 2DPCA for dimensionality reduction. Subsequently, histogram sequences are calculated based on these coefficient features. The final features of face image can be obtained by the fusion of the normalized histogram sequences using weight scheme. Finally, support vector machines is used as classifier. Several experiments on popular face databases such as CAL-PEAL and FERET demonstrate the effectiveness of the proposed method.
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Li, X., Fei, S., Zhang, T. (2009). Face Recognition Based on Histogram of Modular Gabor Feature and Support Vector Machines. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_37
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DOI: https://doi.org/10.1007/978-3-642-01513-7_37
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