NDLI: Kernel locality-constrained sparse coding for head pose estimation

Content Provider	IET Digital Library
Author	Kim, Hyunduk Sohn, Myoung Kyu Kim, Dong Ju Lee, Sang Heon
Abstract	In many situations, it would be practical for a computer system user interface to have a model of where a person is looking and what the user is paying attention to. In this study, the authors describe a novel feature coding method for head pose estimation. The widely-used sparse coding (SC) method encodes a test sample using a sparse linear combination of training samples. However, it does not consider the underlying structure of the data in the feature space. In contrast, locality-constrained linear coding (LLC) utilises locality constraints to project each input data into its local-coordinate system. Based on the recent success of LLC, the authors introduce locality-constrained sparse coding (LSC) to overcome the limitation of Sparse Coding. The authors also propose kernel locality-constrained sparse coding, which is a non-linear extension of LSC. By using kernel tricks, the authors implicitly map the input data into the kernel feature space associated with the kernel function. In experiments, the proposed algorithm was applied to a head pose estimation application. Experimental results demonstrated the increased effectiveness and robustness of the method.
Starting Page	828
Ending Page	835
Page Count	8
ISSN	17519632
Volume Number	10
e-ISSN	17519640
Issue Number	Issue 8, Dec (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/10/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2015.0242
Journal	IET Computer Vision
Publisher Date	2016-05-19
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer System User Interface Computer Vision And Image Processing Technique Feature Coding Method Feature Extraction Head Pose Estimation Image And Video Coding Image Coding Image Recognition Kernel Feature Space Kernel Function Kernel Locality-constrained Sparse Coding Kernel Tricks Local-coordinate System Locality-constrained Linear Coding Pose Estimation Test Sample Training Sample User InterFace
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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