REST database

Citation Author(s):
Adel M.
REGIM-Lab.: REsearch Groups in Intelligent Machines, ENIS, University of Sfax, Tunisia
Submitted by:
Adel Alimi
Last updated:
Thu, 01/07/2021 - 16:10
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Biometric-based hand modality is considered as one of the most popular biometric technologies especially in forensic applications. Hand recognition is an active research topic in recent years and in order to promote hand’s recognition research, the REGIM-Lab.: REsearch Groups in Intelligent Machines, ENIS, University of Sfax, Tunisia provides the REgim Sfax Tunisian hand database (REST database) freely of charge to mainly hand and palmprint recognition researchers.

The acquisition is performed using a low cost digital Camera device. The captured left and right hand images are in RGB and have a size of 1536 * 1250 pixels, in low resolution of 72 dpi. The hands are placed in a comfortable way without any contact nor restriction of pegs or template, and the camera should be placed in front of the hand at approximately 50 cm in order to capture, simultaneously, hand and palmprint modalities. However, users are asked to separate their fingers from each other and change angles between them, during acquisition. The lighting of hand images has been naturally diffused due to illumination variations inside the REGIM laboratory environment. The images are collected from 150 subjects in the age group of 6-70 years, over a period of four months. In order to ensure the success of the image acquisition step, the subjects were just requested to place their hand entirely in front of a uniform dark background.

All documents and papers that uses the REgim Sfax Tunisian hand database (REST database) will acknowledge the use of the database by including an appropriate citation to the following:

Nesrine Charfi, Hanene Trichili, Adel M. Alimi, and Basel Solaiman. "Local invariant representation for multi-instance toucheless palmprint identification." In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 003522-003527. IEEE, 2016.


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For deep learning identification research codes only

Submitted by Geoff Yuen on Wed, 03/03/2021 - 01:27

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