Abstract
Spatial pyramid matching using sparse coding (ScSPM) algorithm can construct the palmprint image descriptors which may effectively express local features and global features of palmprint image. In the paper, we adopt sparse coding and max pooling instead of vector quantization coding and sum pooling to extract descriptors, and it improves the nonlinear coding to linear coding. Then, the linear SVM classifier is applied to replace the nonlinear classifier in pyramid matching. We apply this algorithm to the recognition of palmprint images and exactly analyze the effects of parameters on the recognition, including the size of a complete dictionary and sparse coding parameter. The experimental results illuminate the excellent effectiveness of the ScSPM algorithm for palmprint recognition.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (No. 61202251), the China Postdoctoral Science Foundation (No. 2014M551086), the Liaoning Provincial Natural Science Foundation (No. 201602035), and by the Program for Liaoning Excellent Talents in University (No. LJQ2013133).
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Liu, L., Zhang, J., Yang, A. (2016). Palmprint Recognition via Sparse Coding Spatial Pyramid Matching Representation of SIFT Feature. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham. https://doi.org/10.1007/978-3-319-46654-5_26
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DOI: https://doi.org/10.1007/978-3-319-46654-5_26
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