Superpixel-based Zernike Moments for Palm-print Recognition

Youssef Chahir (1) , Amina Serir (2) , Bilal Attallah (1) , Abdelwahhab Boudjelal (1)

1 Equipe Image - Laboratoire GREYC - UMR6072
2 USTHB - Université des Sciences et de la Technologie Houari Boumediene = University of Sciences and Technology Houari Boumediene [Alger]

In the contemporary period, significant attention has been focused on the prospects of innovative personal recognition methods based on palm print biometrics. However, diminished local consistency and interference from noise are only some of the obstacles that hinder the most common methods of palm-print imaging such as the grey texture and other low-level of the palm. Nevertheless, the development of the process and tackling of the obstacles faced have a potential solution in the form of high-level characteristic imaging for palm-print identification. In this study, Zernike Moments are used for acquiring superpixel features that are spiral scanned images, which is an innovative recognition method. By using the extreme learning machine, the inter- and intra-similarities of the palm-print feature maps are determined. Our experiments yield good results with an accuracy rate of 97.52 and an equal error rate of 1.47 % on the palm-print PolyU database.

Mots clés: image segmentation; palm-print recognition; image matching Reference Biographical notes: (ABS); feature extraction; ELM

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Traitement des images [eess.IV]; Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]; Multimédia [cs.MM]

Youssef Chahir, Amina Serir, Bilal Attallah, Abdelwahhab Boudjelal. Superpixel-based Zernike Moments for Palm-print Recognition. International journal of electronic security and digital forensics, 2019, Vol.11 (No.4), pp.420 - 433. ⟨10.1504/IJESDF.2019.102561⟩. ⟨hal-01877629⟩