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Feature extraction in palmprint recognition using spiral of moment skewness and kurtosis algorithm

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Because of their high recognition rates, coding-based approaches that use multispectral palmprint images have become one of the most popular palmprint recognition methods. This paper describes a new multispectral palmprint recognition method that aims to further improve the performance of coding-based approaches by focusing on the local binary pattern (LBP) filters and spiral moments features. The final feature map is derived through a staged process of creating a composite of spiral and LBP features by fusing them together and passing the features through the minimum redundancy maximum relevance transformers. Using Hamming distances, the inter- and intra-similarities of the palmprint feature maps are determined. The experimental technique was evaluated using the available data on the IITD, MSPolyU and PolyU PPDB databases. The results indicate that the method achieved high levels of accuracy in the identification and verification modes. Furthermore, this method outperforms the existing advanced techniques.

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This work was conducted in the GREYC Laboratory in collaboration with the Algerian Ministry of Higher Education and Scientific Research. We thank our colleagues from the GREYC Laboratory in France who provided insight and expertise, thus greatly assisting the research.

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Correspondence to Bilal Attallah.

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Attallah, B., Serir, A. & Chahir, Y. Feature extraction in palmprint recognition using spiral of moment skewness and kurtosis algorithm. Pattern Anal Applic 22, 1197–1205 (2019).

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