Abstract
In recent years, several scale-invariant features have been proposed in literature, this paper analyzes the usage of Speeded Up Robust Features (SURF) as local descriptors, and as we will see, they are not only scale-invariant features, but they also offer the advantage of being computed very efficiently. Furthermore, a fundamental matrix estimation method based on the RANSAC is applied. The proposed approach allows to match faces under partial occlusions, and even if they are not perfectly aligned. Thus based on the above advantages of SURF, we propose to exploit SURF features in face recognition since current approaches are too sensitive to registration errors and usually rely on a very good initial alignment and illumination of the faces to be recognized.
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Carro, R.C., Larios, JM.A., Huerta, E.B., Caporal, R.M., Cruz, F.R. (2015). Face Recognition Using SURF. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_31
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DOI: https://doi.org/10.1007/978-3-319-22180-9_31
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