Cited 2 time in webofscience Cited 3 time in scopus

Kernel locality-constrained sparse coding for head pose estimation

Title
Kernel locality-constrained sparse coding for head pose estimation
Authors
Kim, HyundukSohn, Myoung-KyuKim, Dong-JuLee, Sang-Heon
DGIST Authors
Sohn, Myoung-KyuLee, Sang-Heon
Issue Date
2016-12
Citation
IET Computer Vision, 10(8), 828-835
Type
Article
Article Type
Article
Keywords
Codes (Symbols)Face RecognitionFeature CodingHead Pose EstimationImageInput Output ProgramsKernel FunctionLinear CodingLinear CombinationsLocal Coordinate SystemRepresentationimage RecognitionSparse CodingSupport Vector MachinesTraining SampleUser Interfaces
ISSN
1751-9632
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. © The Institution of Engineering and Technology.
URI
http://hdl.handle.net/20.500.11750/5060
DOI
10.1049/iet-cvi.2015.0242
Publisher
Institution of Engineering and Technology
Related Researcher
Files:
There are no files associated with this item.
Collection:
Convergence Research Center for Future Automotive Technology1. Journal Articles


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