Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Person-Independent Head Pose Estimation Using Biased Manifold Embedding | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 283540 15 pages doi 2008 283540 Research Article Person-Independent Head Pose Estimation Using Biased Manifold Embedding Vineeth Nallure Balasubramanian Sreekar Krishna and Sethuraman Panchanathan Center for Cognitive Ubiquitous Computing Arizona State University Tempe AZ 85281 USA Correspondence should be addressed to Vineeth Nallure Balasubramanian Received 2 June 2007 Revised 16 September 2007 Accepted 12 November 2007 Recommended by Konstantinos N. Plataniotis Head pose estimation has been an integral problem in the study of face recognition systems and human-computer interfaces as part of biometric applications. A fine estimate of the head pose angle is necessary and useful for several face analysis applications. To determine the head pose face images with varying pose angles can be considered to be lying on a smooth low-dimensional manifold in high-dimensional image feature space. However when there are face images of multiple individuals with varying pose angles manifold learning techniques often do not give accurate results. In this work we propose a framework for a supervised form of manifold learning called Biased Manifold Embedding to obtain improved performance in head pose angle estimation. This framework goes beyond pose estimation and can be applied to all regression applications. This framework although formulated for a regression scenario unifies other supervised approaches to manifold learning that have been proposed so far. Detailed studies of the proposed method are carried out on the FacePix database which contains 181 face images each of 30 individuals with pose angle variations at a granularity of 1 . Since biometric applications in the real world may not contain this level of granularity in training data an analysis of the methodology is performed on sparsely sampled data to validate its effectiveness. We obtained
