Transfer Learning Using VGG Based on Deep Convolutional Neural Network For Finger-Knuckle-Print Recognition

Hamidi, Amira; Khemgani, Salma; Bensid, Khled

Transfer Learning Using VGG Based on Deep Convolutional Neural Network For Finger-Knuckle-Print Recognition

Files

Transfer Learning Using VGG Based on Deep Convolutional Neural Network For Finger-Knuckle-Print Recognition.pdf (496.18 KB)

Date

2021-05-25

Authors

Hamidi, Amira

Khemgani, Salma

Bensid, Khled

Publisher

University of Oum El Bouaghi

Abstract

Transfer learning is an example of Convolutional Neural Network (CNN) method. It based to reusing a pretrained model knowledge for another task. which used for image classification, feature extraction, and clustering problems. In this paper, we used two types of the pre-trained models VGG–16 and VGG-19 with deep convolutional neural network to extract the features of Finger-Knuckle-Print FKP images in order to develop an efficient multimodal identification system. The results obtained in this work show an excellent performance for unimodal and multimodal identification systems.

Keywords

Tansfer learning; convolutional neural network (CNN); VGG-16; VGG-19; finger-knuckle-print (FKP).

URI

http://dspace.univ-oeb.dz:4000/handle/123456789/18737

Collections

The Second International Conference on Computer Science's Complex Systems and their Applications (Oum El Bouaghi, Algeria, May 25-26, 2021)

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