TY - JOUR
T1 - Kernel discriminant transformation for image set-based face recognition
AU - Chu, Wen Sheng
AU - Chen, Ju Chin
AU - Lien, Jenn Jier James
N1 - Funding Information:
This work was supported by the National Science Council , Republic of China, Taiwan, under Contract NSC-99-2221-E-006-216 . Wen-Sheng Chu is a full-time research assistant in the Robotics Institute at Carnegie Mellon University, Pittsburgh, PA. He received his B.S. and M.S. degrees in computer science and information engineering from National Cheng Kung University in 2005 and 2007, respectively. His research interests mainly focus on computer vision and pattern recognition problems, especially those related to automatic face recognition, image retrieval, gender classification and common pattern discovery. Ju-Chin Chen received her B.S., M. S. and Ph.D. degrees in Computer Science and Information Engineering from National Cheng Kung University, Tainan, Taiwan, in 2002, 2004 and 2010, respectively. She is now an assistant professor in the Department of Computer Science and Information Engineering at National Kaohsiung University of Applied Science, Taiwan. Her research interests lie in the fields of machine learning, computer vision and pattern recognition. Jenn-Jier James Lien (M’00) received his M.S. and Ph.D. degrees in electrical engineering from Washington University, St. Louis, MO, and the University of Pittsburgh, Pittsburgh, PA, in 1993 and 1998, respectively. From 1995 to 1998, he was a research assistant at the Vision Autonomous Systems Center in the Robotics Institute at Carnegie Mellon University, Pittsburgh, PA. From 1999 to 2002, he was a senior research scientist at L1-Identity (formerly Visionics) and a project lead for the DARPA surveillance project. He is now an associate professor in the department of computer science and information engineering at National Cheng Kung University, Taiwan.
PY - 2011/8
Y1 - 2011/8
N2 - This study presents a novel kernel discriminant transformation (KDT) algorithm for face recognition based on image sets. As each image set is represented by a kernel subspace, we formulate a KDT matrix that maximizes the similarities of within-kernel subspaces, and simultaneously minimizes those of between-kernel subspaces. Although the KDT matrix cannot be computed explicitly in a high-dimensional feature space, we propose an iterative kernel discriminant transformation algorithm to solve the matrix in an implicit way. Another perspective of similarity measure, namely canonical difference, is also addressed for matching each pair of the kernel subspaces, and employed to simplify the formulation. The proposed face recognition system is demonstrated to outperform existing still-image-based as well as image set-based face recognition methods using the Yale Face database B, Labeled Faces in the Wild and a self-compiled database.
AB - This study presents a novel kernel discriminant transformation (KDT) algorithm for face recognition based on image sets. As each image set is represented by a kernel subspace, we formulate a KDT matrix that maximizes the similarities of within-kernel subspaces, and simultaneously minimizes those of between-kernel subspaces. Although the KDT matrix cannot be computed explicitly in a high-dimensional feature space, we propose an iterative kernel discriminant transformation algorithm to solve the matrix in an implicit way. Another perspective of similarity measure, namely canonical difference, is also addressed for matching each pair of the kernel subspaces, and employed to simplify the formulation. The proposed face recognition system is demonstrated to outperform existing still-image-based as well as image set-based face recognition methods using the Yale Face database B, Labeled Faces in the Wild and a self-compiled database.
UR - https://www.scopus.com/pages/publications/79953048840
UR - https://www.scopus.com/pages/publications/79953048840#tab=citedBy
U2 - 10.1016/j.patcog.2011.02.011
DO - 10.1016/j.patcog.2011.02.011
M3 - Article
AN - SCOPUS:79953048840
SN - 0031-3203
VL - 44
SP - 1567
EP - 1580
JO - Pattern Recognition
JF - Pattern Recognition
IS - 8
ER -