Abstract
In terms of supervised face recognition, linear discriminant analysis (LDA) has been viewed as one of the most popular approaches during the past years. In this paper, taking advantage of the equivalence between LDA and the least square problem, we propose a new fusion method for face classification, based on the combination of least square solutions for local mean and local texture into multiple optimization problems. Extensive experiments on AR_Gray and Yale face database indicate the competitive performance of the proposed method, compared to the traditional LDA.
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Tao, Y., Yang, J. (2012). Fusion of Local Features for Face Recognition by Multiple Least Square Solutions. In: Zheng, WS., Sun, Z., Wang, Y., Chen, X., Yuen, P.C., Lai, J. (eds) Biometric Recognition. CCBR 2012. Lecture Notes in Computer Science, vol 7701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35136-5_2
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DOI: https://doi.org/10.1007/978-3-642-35136-5_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35135-8
Online ISBN: 978-3-642-35136-5
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