This paper investigates head pose estimation problem which is considered as front-end preprocessing for improving multi-view human face recognition. We propose a computational model for perceiving head pose based on neurophysiologi-cal plausible invariance representation. In order to obtain the invariance representa tion bases or facial multi-view bases, a learning algorithm is derived for training the linear representation model. Then the facial multi-view bases are used to construct the computational model for head pose perception. The measure for head pose per ception is introduced that the final-layered winner neuron gives the resulting head pose, if its connected pre-layer has the most firing neurons. Computer simulation results and comparisons show that the proposed model achieves satisfactory accuracy for head pose estimation of facial multi-view images in the CAS-PEAL face database.
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Yang, W., Zhang, L. (2008). Head Pose Perception Based on Invariance Representation. In: Mahr, B., Huanye, S. (eds) Autonomous Systems – Self-Organization, Management, and Control. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8889-6_1
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DOI: https://doi.org/10.1007/978-1-4020-8889-6_1
Publisher Name: Springer, Dordrecht
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