Abstract
Feature extraction is one of the hot topics in face recognition. However, many face extraction methods will suffer from the “small sample size” problem, such as Linear Discriminant Analysis (LDA). Direct Linear Discriminant Analysis (DLDA) is an effective method to address this problem. But conventional DLDA algorithm is often computationally expensive and not scalable. In this paper, DLDA is analyzed from a new viewpoint via QR decomposition and an efficient and robust method named DLDA/QR algorithm is proposed. The proposed algorithm achieves high efficiency by introducing the QR decomposition on a small-size matrix, while keeping competitive classification accuracy. Experimental results on ORL face database demonstrate the effectiveness of the proposed method.
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Zheng, YJ., Guo, ZB., Yang, J., Wu, XJ., Yang, JY. (2007). DLDA/QR: A Robust Direct LDA Algorithm for Face Recognition and Its Theoretical Foundation. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_37
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DOI: https://doi.org/10.1007/978-3-540-71701-0_37
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