Abstract
Contourlet transform can obtain the better contour of an image and make it sparser in local subspace. While independent component analysis based winner-take-all (WTA-ICA) algorithm can extract efficiently image features and is simpler and faster under high dimensional computational requirements. Therefore, combined the advantages of the two algorithms, a new palmprint feature extraction method utilizing WTA-ICA in contourlet transform domain is discussed in this paper. First, each test image selected from PolyU palmprint database is preprocessed by using contourlet transform to obtain low frequency and high frequency sub-band images in given layers, and high frequency sub- band images are denoised by the wavelet method. Then the WTA-ICA algorithm is used to train the low and high frequency sub-bands to obtain the low and high frequency features. Further, considered feature fusion method for the low and high features as well as palmprint original WTA-ICA features, the palmprint feature extraction task can be well realized.
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This work was supported by the National Nature Science Foundation of China (Grant No. 61972002).
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Shang, L., Zhang, Y., Sun, Zl. (2022). Palmprint Feature Extraction Utilizing WTA-ICA in Contourlet Domain. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13393. Springer, Cham. https://doi.org/10.1007/978-3-031-13870-6_39
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DOI: https://doi.org/10.1007/978-3-031-13870-6_39
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