Abstract
An improved feature extraction for face recognition is presented in this paper. In the proposed technique, the input face image is divided into blocks and two-dimensional Discrete Cosine Transform (DCT) approach is applied to each block. Then the low frequencies of all two-dimensional DCT coefficients from each block are extracted and combined to form a feature vector. Thereafter, weighted generalized kernel Fisher discriminant is performed on these vectors. Experimental results on the ORL face database demonstrate the effectiveness of the proposed method.
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Zou, J., Sun, F. (2012). Face Recognition Using Block-Based DCT and Weighted Generalized KFD. In: Luo, J. (eds) Affective Computing and Intelligent Interaction. Advances in Intelligent and Soft Computing, vol 137. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27866-2_30
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DOI: https://doi.org/10.1007/978-3-642-27866-2_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27865-5
Online ISBN: 978-3-642-27866-2
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