Abstract
In this paper, a two–level supervised feature selection algorithm for local feature–based face recognition is presented. In the first part, a genetic algorithm is used to determine the useful locations of the face region for recognition. 2D Gabor wavelet–based feature extractors are used for local image descriptors at these locations. In the second part, the most useful frequencies and orientations of Gabor kernels are determined using a floating feature selection algorithm. Our major aim in this study is to examine the relevance of the two common assumptions in the local feature based face recognition literature: first, that the contribution of a specific feature to the recognition performance is independent of others, and secondly, that feature extractors should be placed over the visually salient points. In this paper, we show that one can obtain better recognition accuracy by relaxing these two assumptions.
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© 2005 Springer-Verlag Berlin Heidelberg
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Gökberk, B., Irfanoglu, M.O., Akarun, L., Alpaydın, E. (2005). Selection of Location, Frequency, and Orientation Parameters of 2D Gabor Wavelets for Face Recognition. In: Tistarelli, M., Bigun, J., Grosso, E. (eds) Advanced Studies in Biometrics. Lecture Notes in Computer Science, vol 3161. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11493648_9
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DOI: https://doi.org/10.1007/11493648_9
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