Abstract
Face recognition is a fundamental capability of humans to recognize each other, which predominantly made its way into computing domain. The demand for fast and highly reliable face recognition methods is as high as ever. This paper proposes one solution based on a novel similarity measure for frontal facial image recognition, which can be computed rapidly while maintaining a high recognition performance. The new proposed similarity measure is named Integrated Random Local Similarity (IRLS), based on an appropriate combination of holistic similarity of facial prominent points expressed in binary image and pixel-wise local similarity of local regions in original gray-level image. The holistic similarity is estimated by the ratio of intersection of the candidate image and the gallery image to the sum of the candidate images prominent points in original gray-level image spatial domain. Experiments have been conducted on AR database. The preliminary experiment results shows that IRLS is a very robust approach which maintains high recognition performance, and deserves to be investigated with larger dataset.
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Huang, H.H.M., Gavrilova, M.L. (2013). Integrated Random Local Similarity Approach for Facial Image Recognition. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2013. ICCSA 2013. Lecture Notes in Computer Science, vol 7972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39643-4_11
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DOI: https://doi.org/10.1007/978-3-642-39643-4_11
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