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On the utility of canonical correlation analysis for domain adaptation in multi-view headpose estimation

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Abstract

The utility of canonical correlation analysis (CCA) for domain adaptation (DA) in the context of multi-view head pose estimation is examined in this work. We consider the three problems studied in [1], where different DA approaches are explored to transfer head pose-related knowledge from an extensively labeled source dataset to a sparsely labeled target set, whose attributes are vastly different from the source. CCA is found to benefit DA for all the three problems, and the use of a covariance profile-based diagonality score (DS) also improves classification performance with respect to a nearest neighbor (NN) classifier.

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Anoop, K. R., Subramanian, R., Vonikakis, V., Ramakrishnan, K. R., & Winkler, S. (2015). On the utility of canonical correlation analysis for domain adaptation in multi-view headpose estimation. In Proceedings - International Conference on Image Processing, ICIP (Vol. 2015-December, pp. 4708–4712). IEEE Computer Society. https://doi.org/10.1109/ICIP.2015.7351700

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