Open AccessStudy of Deep Learning Methods f or Fingerprint Recognition
Author(s) -
Mamadou Diarra,
Kacoutchy Jean AYIKPA,
Ballo Abou Bakary,
Kouassi Brou Medard,
Teacher
Publication year - 2021
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c6478.0910321
Subject(s) - computer science , artificial intelligence , biometrics , rgb color model , grayscale , password , fingerprint (computing) , preprocessor , fingerprint recognition , pattern recognition (psychology) , deep learning , authentication (law) , computer vision , convolution (computer science) , image (mathematics) , artificial neural network , computer security
Biometric systems aim to reliably identify and authenticate an individual using physiological or behavioral characteristics. Traditional systems such as the use of access cards, passwords have shown limitations such as forgotten passwords, stolen cards, etc. As an alternative, biometric systems present themselves as efficient systems with a high reliability due to the physiological characteristics of each individual. This paper focuses on a deep learning method for fingerprint recognition. The described architecture uses a pre-processing phase in which grayscale images are represented on the RGB bands and then merged to obtain color images. On the obtained color images will be extracted the characteristics of the fingerprints textures.The fingerprint images after preprocessing are used in a deep convolution network system for decision making. The method is robust with an accuracy of over 99.43% and 99.53% with the respective variants densenet-201 and ResNet-50.