An adaptation framework for head-pose classification in dynamic multi-view scenarios

Authors:
Anoop K. Rajagopal;Ramanathan Subramanian;Radu L. Vieriu;Elisa Ricci;Oswald Lanz;Kalpathi Ramakrishnan;Nicu Sebe
Affiliations:
Indian Institute of Science, Bangalore, India;Department of Computer Science and Information Engineering (DISI), Trento, Italy;-;Department of Electrical and Information Engineering, University of Perugia, Italy;Fondazione Bruno Kessler, Trento, Italy;Indian Institute of Science, Bangalore, India;Department of Computer Science and Information Engineering (DISI), Trento, Italy
Venue:
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Year:
2012

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Abstract

Multi-view head-pose estimation in low-resolution, dynamic scenes is difficult due to blurred facial appearance and perspective changes as targets move around freely in the environment. Under these conditions, acquiring sufficient training examples to learn the dynamic relationship between position, face appearance and head-pose can be very expensive. Instead, a transfer learning approach is proposed in this work. Upon learning a weighted-distance function from many examples where the target position is fixed, we adapt these weights to the scenario where target positions are varying. The adaptation framework incorporates reliability of the different face regions for pose estimation under positional variation, by transforming the target appearance to a canonical appearance corresponding to a reference scene location. Experimental results confirm effectiveness of the proposed approach, which outperforms state-of-the-art by 9.5% under relevant conditions. To aid further research on this topic, we also make DPOSE- a dynamic, multi-view head-pose dataset with ground-truth publicly available with this paper.