Tham khảo tài liệu 'báo cáo hóa học: " facial expression recognition using local binary patterns and discriminant kernel locally linear embedding"', luận văn - báo cáo phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | EURASIP Journal on Advances in Signal Processing SpringerOpen0 This Provisional PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text HTML versions will be made available soon. Facial expression recognition using local binary patterns and discriminant kernel locally linear embedding EURASIP Journal on Advances in Signal Processing 2012 2012 20 doi 1687-6180-2012-20 Xiaoming Zhao tzxyzxm@ Shiqing Zhang tzczsq@ ISSN 1687-6180 Article type Research Submission date 4 October 2011 Acceptance date 27 January 2012 Publication date 27 January 2012 Article URL http content 2012 1 20 This peer-reviewed article was published immediately upon acceptance. It can be downloaded printed and distributed freely for any purposes see copyright notice below . For information about publishing your research in EURASIP Journal on Advances in Signal Processing go to http authors instructions For information about other SpringerOpen publications go to http 2012 Zhao and Zhang licensee Springer. This is an open access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Facial expression recognition using local binary patterns and discriminant kernel locally linear embedding Xiaoming Zhao1 and Shiqing Zhang 2 department of Computer Science Taizhou University Taizhou 318000 . China 2School of Physics and Electronic Engineering Taizhou University Taizhou 318000 . China Corresponding author tzczsq@ E-mail address XZ tzxyzxm@ Abstract Given the nonlinear manifold structure of facial images a new kernel-based supervised manifold learning algorithm based on locally linear embedding LLE called discriminant kernel locally linear embedding DKLLE is proposed for .