Title :
(2D)2UDP: A New Two-Directional Two-Dimensional Unsupervised Discriminant Projection for Face Recognition
Author :
Li, Yong-zhi ; He, Guang-ming ; Yang, Jing-Yu ; Wang, Yu-ping
Author_Institution :
Sch. of Inf. Sci. & Technol., Nanjing Forestry Univ., Nanjing
Abstract :
Based on manifold learning, a new feature extraction method is proposed for face recognition in the paper. The new method is called two-directional two-dimensional unsupervised discriminant projection ((2D)2UDP), which simultaneously works image matrix in the row direction and in the column direction for feature extraction. The experimental results on ORL face databases and AR face databases indicate that the proposed method has higher recognition rate and more stable.
Keywords :
face recognition; feature extraction; learning (artificial intelligence); AR face databases; ORL face databases; face recognition; feature extraction method; image matrix; manifold learning; two-directional two-dimensional unsupervised discriminant projection; Face recognition; Feature extraction; Forestry; Image databases; Information science; Laplace equations; Paper technology; Principal component analysis; Scattering; Spatial databases; face recognition; feature extraction; manifold-learning techniques; unsupervised discriminant projection;
Conference_Titel :
Natural Computation, 2008. ICNC '08. Fourth International Conference on
Conference_Location :
Jinan
Print_ISBN :
978-0-7695-3304-9
DOI :
10.1109/ICNC.2008.657
