Finger-Knuckle-Print Recognition Using ...
Document type :
Communication dans un congrès avec actes
Title :
Finger-Knuckle-Print Recognition Using Deep Convolutional Neural Network
Author(s) :
Trabelsi, Selma [Auteur]
Samai, Djamel [Auteur]
Meraoumia, Abdallah [Auteur]
Bensid, Khaled [Auteur]
Benlamoudi, Azeddine [Auteur]
Dornaika, Fadi [Auteur]
Tahleb Ahmed, Abdelmalik [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 [IEMN-DOAE]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Samai, Djamel [Auteur]
Meraoumia, Abdallah [Auteur]
Bensid, Khaled [Auteur]
Benlamoudi, Azeddine [Auteur]
Dornaika, Fadi [Auteur]
Tahleb Ahmed, Abdelmalik [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 [IEMN-DOAE]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Conference title :
1st International Conference on Communications, Control Systems and Signal Processing (CCSSP 2020 )
City :
EL OUED
Country :
Algérie
Start date of the conference :
2020-05-16
Publisher :
IEEE
English keyword(s) :
Feature extraction
Fingers
Convolutional neural networks
Indexes
Biomedical imaging
Training
Fingers
Convolutional neural networks
Indexes
Biomedical imaging
Training
HAL domain(s) :
Informatique [cs]
Sciences de l'ingénieur [physics]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Réseaux et télécommunications [cs.NI]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Sciences de l'ingénieur [physics]/Electronique
Sciences de l'ingénieur [physics]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Réseaux et télécommunications [cs.NI]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Sciences de l'ingénieur [physics]/Electronique
English abstract : [en]
Biometric technology has become essential in our daily life. In such a biometric system, personal identification is based on behavioral or biological characteristics. Recently, the trait of the Finger-Knuckle-Print (FKP) ...
Show more >Biometric technology has become essential in our daily life. In such a biometric system, personal identification is based on behavioral or biological characteristics. Recently, the trait of the Finger-Knuckle-Print (FKP) is used due to its ease of use and low cost. In order to develop an efficient recognition system based on these images, we propose a deep learning method where we use our own Convolutional Neural Network (CNN) to identify persons. Excellent results were conducted with unimodal and multimodal identification systems.Show less >
Show more >Biometric technology has become essential in our daily life. In such a biometric system, personal identification is based on behavioral or biological characteristics. Recently, the trait of the Finger-Knuckle-Print (FKP) is used due to its ease of use and low cost. In order to develop an efficient recognition system based on these images, we propose a deep learning method where we use our own Convolutional Neural Network (CNN) to identify persons. Excellent results were conducted with unimodal and multimodal identification systems.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Comment :
ISBN 978-1-7281-5836-5 ; e-ISBN 978-1-7281-5835-8
Source :