Kernel locality-constrained sparse coding for head pose estimation
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.