ISSN (Online) : 2456 - 0774

Email :

ISSN (Online) 2456 - 0774



Abstract: - Biometric systems based on touch less and less constrained palm print are being increasingly studied since they allow a favorable trade-off between high-accuracy and high usability recognition. Another advantage is that with a palmar hand acquisition, it is possible to extract the palm print as well as the Inner Finger Texture (IFT) and increase the recognition accuracy without requiring further biometric acquisitions. Recently, most methods in the literature consider Deep Learning (DL) and Convolutional Neural Networks (CNN), due to their high recognition accuracy and the capability to adapt to biometric samples captured in heterogeneous and less-constrained conditions. However, current methods based on DL do not consider the fusion of palm print with IFT. In this work, we propose the first novel method in the literature based on a CNN to perform the fusion of palm print and IFT using a single hand acquisition. Our approach uses an innovative procedure based on training the same CNN topology separately on the palm print and the IFT, adapting the neural model to the different biometric traits, and then performing a feature-level fusion. We validated the proposed methodology on a public database captured in touch less and less constrained conditions, with results showing that the fusion enabled to increase the recognition accuracy, without requiring multiple biometric acquisitions.

Keywords: - Deep Learning, CNN, Palm print, Finger

Full Text PDF


Submit paper at

Paper Submission Open For April  20212
UGC indexed in (Old UGC) 2017
Last date for paper submission 30th April, 2022
Deadline Submit Paper any time
Publication of Paper Within 01-02 Days after completing all the formalities
Paper Submission Open For Publication /online Conference 
Publication Fees  
Free for PR Students