Deep-Analysis of Palmprint Representation based on Correlation Concept for Human Biometrics identification

Raouia Mokni (1) , Hassen Drira (2) , Monji Kherallah (3)

1 PSAU - Prince Sattam Bin Abdul-Aziz University
2 IMT Lille Douai - Ecole nationale supérieure Mines-Télécom Lille Douai
3 Université de Sfax - University of Sfax

The security of persons requires a beefy guarantee in our society particularly, with the spread of terrorism nowadays throughout the world. In this context, palmprint identification based on texture analysis is amongst the triumphal pattern recognition applications to recognize the persons. In this paper, we investigated a deep texture analysis for the palmprint texture pattern representation based on a fusion between several texture information extracting through multiple descriptors, such as "HOG and Gabor Filters", "Fractal dimensions" and "GLCM" corresponding respectively to the Frequency, Model, and Statistical methodologies-based texture features. We assessed the proposed deep texture analysis method as well as the applicability of the dimensionality reduction techniques and the correlation concept between the features-based fusion on the challenging PolyU, CASIA and IIT-Delhi Palmprint databases. The experimental results show that the fusion of different texture types using the correlation concept for palmprint modality identification leads to promising results.

Mots clés: Palmprint; Biometric; Deep-Analysis; GDA; Fusion; Correlation Concept; MCCA; Classification; Random Forest

Domaines: Sciences de l'information et de la communication; Vision par ordinateur et reconnaissance de formes [cs.CV]

Raouia Mokni, Hassen Drira, Monji Kherallah. Deep-Analysis of Palmprint Representation based on Correlation Concept for Human Biometrics identification. International Journal of Digital Crime and Forensics, 2020. ⟨halshs-03147087⟩