TY - GEN
T1 - 3D Face recognition based on local shape patterns and sparse representation classifier
AU - Huang, Di
AU - Ouji, Karima
AU - Ardabilian, Mohsen
AU - Wang, Yunhong
AU - Chen, Liming
PY - 2011
Y1 - 2011
N2 - In recent years, 3D face recognition has been considered as a major solution to deal with these unsolved issues of reliable 2D face recognition, i.e. illumination and pose variations. This paper focuses on two critical aspects of 3D face recognition: facial feature description and classifier design. To address the former one, a novel local descriptor, namely Local Shape Patterns (LSP), is proposed. Since LSP operator extracts both differential structure and orientation information, it can describe local shape attributes comprehensively. For the latter one, Sparse Representation Classifier (SRC) is applied to classify these 3D shape-based facial features. Recently, SRC has been attracting more and more attention of researchers for its powerful ability on 2D image-based face recognition. This paper continues to investigate its competency in shape-based face recognition. The proposed approach is evaluated on the IV2 3D face database containing rich facial expression variations, and promising experimental results are achieved which prove its effectiveness for 3D face recognition and insensitiveness to expression changes.
AB - In recent years, 3D face recognition has been considered as a major solution to deal with these unsolved issues of reliable 2D face recognition, i.e. illumination and pose variations. This paper focuses on two critical aspects of 3D face recognition: facial feature description and classifier design. To address the former one, a novel local descriptor, namely Local Shape Patterns (LSP), is proposed. Since LSP operator extracts both differential structure and orientation information, it can describe local shape attributes comprehensively. For the latter one, Sparse Representation Classifier (SRC) is applied to classify these 3D shape-based facial features. Recently, SRC has been attracting more and more attention of researchers for its powerful ability on 2D image-based face recognition. This paper continues to investigate its competency in shape-based face recognition. The proposed approach is evaluated on the IV2 3D face database containing rich facial expression variations, and promising experimental results are achieved which prove its effectiveness for 3D face recognition and insensitiveness to expression changes.
KW - 3D face recognition
KW - Local Shape Patterns (LSP)
KW - Sparse Representation Classifier (SRC)
KW - local descriptor
UR - https://www.scopus.com/pages/publications/78751663255
U2 - 10.1007/978-3-642-17832-0_20
DO - 10.1007/978-3-642-17832-0_20
M3 - 会议稿件
AN - SCOPUS:78751663255
SN - 3642178316
SN - 9783642178313
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 206
EP - 216
BT - Advances in Multimedia Modeling - 17th International Multimedia Modeling Conference, MMM 2011, Proceedings
T2 - 17th Multimedia Modeling Conference, MMM 2011
Y2 - 5 January 2011 through 7 January 2011
ER -