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Boosting-based transfer learning for multi-view head-pose classification from surveillance videos

Vieriu, Radu L and Rajagopal, Anoop K and Lanz, Oswald and Subramanian, Ramanathan and Ricci, Elisa and Ramakrishnan, Kalpathi and Sebe, Nicu (2012) Boosting-based transfer learning for multi-view head-pose classification from surveillance videos. In: 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO), 27-31 Aug. 2012, Bucharest, Romania.

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This work proposes a boosting-based transfer learning approach for head-pose classification from multiple, low-resolution views. Head-pose classification performance is adversely affected when the source (training) and target (test) data arise from different distributions (due to change in face appearance, lighting, etc). Under such conditions, we employ Xferboost, a Logitboost-based transfer learning framework that integrates knowledge from a few labeled target samples with the source model to effectively minimize misclassifications on the target data. Experiments confirm that the Xferboost framework can improve classification performance by up to 6%, when knowledge is transferred between the CLEAR and FBK four-view headpose datasets.

Item Type: Conference Paper
Publisher: IEEE
Additional Information: Copyright of this article belongs to IEEE.
Keywords: Multi-View Headpose Classification; Low-Resolution; Xferboost; Boosting-Based Transfer Learning
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 22 May 2013 07:05
Last Modified: 22 May 2013 07:05
URI: http://eprints.iisc.ac.in/id/eprint/46550

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