Abstract
In this paper, a new model called FLD-SIFT is devised for compact representation and accurate recognition of faces. Unlike scale invariant feature transform model that uses smoothed weighted histogram and massive dimension of feature vectors, in the proposed model, an image patch centered around the keypoint has been considered and linear discriminant analysis (FLD) is employed for compact representation of image patches. Contrasting to PCA-SIFT model that employs principal component analysis (PCA) on a normalized gradient patch, we employ FLD on an image patch exists around the keypoints. The proposed model has better computing performance in terms of recognition time than the basic SIFT model. To establish the superiority of the proposed model, we have experimentally compared the performance of our new algorithm with (2D)2-PCA, (2D)2-FLD and basic SIFT model on the AT&T face database.
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Shekar, B.H., Sharmila Kumari, M., Mestetskiy, L.M., Dyshkant, N. (2011). FLD-SIFT: Class Based Scale Invariant Feature Transform for Accurate Classification of Faces. In: Das, V.V., Stephen, J., Chaba, Y. (eds) Computer Networks and Information Technologies. CNC 2011. Communications in Computer and Information Science, vol 142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19542-6_3
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DOI: https://doi.org/10.1007/978-3-642-19542-6_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19541-9
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