Head Pose Estimation Based on Image Abstraction for Multiclass Classification
We address the problem of head pose estimation from a facial RGB image as a multiclass classification problem. Head pose estimation continues to be a challenge for computer vision systems due to extraneous characteristics and factors that do not contain pose information and affect changing pixel values in a facial image. To achieve robustness against variations in identity, illumination condition, and facial expression, we propose an image abstraction method that can reduce unnecessary information and emphasize important information for facial pose classification. Experiments are conducted to verify that our head pose estimation algorithm is robust against variations in the input images.
KeywordsHead pose estimation Image abstraction Multiclass classification
This work was supported by the IT R&D program of MKE & KEIT (10041610, The development of the recognition technology for user identity, behavior and location that has a performance approaching recognition rates of 99 % on 30 people by using perception sensor network in the real environment). This work was also supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (MEST) (2011-0013776).
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