Abstract
Sparse representation (SR) can effectively represent structure features of images and has been used in image processing field. A new palmprint image classification method by using multiple kernel sparse representation (MKSR) is proposed in this paper. Kernel sparse representation (KSR) behaves good robust and occlusion like as sparse representation (SR) methods. Especially, KSR behaves better classification property than common sparse representation methods and used widely in pattern recognition task. In KSR based classification methods, the selection of a kernel function and its parameters is very important. Usually, the kernel selected is not the most suitable and can not contain complete information. Therefore, MKSR methods are developed currently and used widely in image classification task. Here, multiple kernel functions select the weighted of Gauss kernel and polynomial kernel. In test, all palmprint images are selected from PolyU palmprint database. The palm classification task is implemented by the extreme learning machine (ELM) classifier. Compared with methods of SR and single kernel based SR, experimental results show that our method proposed has better calcification performance.
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Acknowledgement
This work was supported by the grants from National Nature Science Foundation of China (No. 61373098), and the science and technology planning project of Suzhou (No. SZP201310).
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Su, Pg., Liu, T. (2016). Palm Image Classification Using Multiple Kernel Sparse Representation Based Dictionary Learning. In: Huang, DS., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2016. Lecture Notes in Computer Science(), vol 9773. Springer, Cham. https://doi.org/10.1007/978-3-319-42297-8_13
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DOI: https://doi.org/10.1007/978-3-319-42297-8_13
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