Skip to Main content Skip to Navigation
Journal articles

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

Bilal Attallah 1 Amina Serir 1 Youssef Chahir 1 
1 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image et Instrumentation de Caen
Abstract : 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.
Complete list of metadata

Cited literature [29 references]  Display  Hide  Download
Contributor : Youssef Chahir Connect in order to contact the contributor
Submitted on : Tuesday, September 18, 2018 - 12:18:53 PM
Last modification on : Saturday, June 25, 2022 - 9:52:36 AM


Files produced by the author(s)



Bilal Attallah, Amina Serir, Youssef Chahir. Feature extraction in palmprint recognition using spiral of moment skewness and kurtosis algorithm. Pattern Analysis and Applications, Springer Verlag, 2019, 22 (3), pp.1197--1205. ⟨10.1007/s10044-018-0712-5⟩. ⟨hal-01790989⟩



Record views


Files downloads