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Statistical Framework for Facial Pose Classification

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Advances in Artificial Intelligence (MICAI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7629))

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Abstract

Pose classification is one of the important steps in some pose invariant face recognition methods. In this paper, we propose to use: (i) Partial least square (PLS) and (ii) Linear regression for facial pose classification. The performance of these two approaches is compared with two edge based approaches and pose-eigenspace approach in terms of classification accuracy. Experimental results on two publicly available face databases (PIE and FERET) show that the regression based approach outperforms other approaches for both the databases.

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Jaiswal, A., Kumar, N., Agrawal, R.K. (2013). Statistical Framework for Facial Pose Classification. In: Batyrshin, I., González Mendoza, M. (eds) Advances in Artificial Intelligence. MICAI 2012. Lecture Notes in Computer Science(), vol 7629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37807-2_8

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  • DOI: https://doi.org/10.1007/978-3-642-37807-2_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37806-5

  • Online ISBN: 978-3-642-37807-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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