Individual Recognition Based on Multi-Spectrum Palm Images

A.Prof.Dr.Raid Rafi Omar Al-Nima
Technical College of Engineering for
 Computer and Artificial Intelligence
 Mosul, Northern Technical University
Mosul, Iraq
عنوان البريد الإلكتروني هذا محمي من روبوتات السبام. يجب عليك تفعيل الجافاسكربت لرؤيته. 

Marwan Khaleel Majeed ALali
Northern Technical University
Mosul, Iraq
عنوان البريد الإلكتروني هذا محمي من روبوتات السبام. يجب عليك تفعيل الجافاسكربت لرؤيته.

Saif Saaduldeen Ahmed
Al-Imam Al-Adham University College
Nineveh, Iraq
عنوان البريد الإلكتروني هذا محمي من روبوتات السبام. يجب عليك تفعيل الجافاسكربت لرؤيته.

Keywords: Deep Learning, Full-Palm Images, Multi-spectrums, Textures, Veins

Abstract
Individual recognition based on palm images is such an interesting subject. In this paper, multi-spectrums for full-palm images are considered. The spectra of 460nm and 940nm are employed. Each spectrum provides special features. That is, the spectrum of 460nm affords full-palm textures, and the spectrum of 940nm presents full-palm veins. These facilities are utilized here, where an Artificial Intelligence (AI) approach is suggested. The suggested approach consists of four Deep Learning (DL) networks. Each network is determined for a certain full-palm image, as there are four types of images: right hand full-palm textures, left hand full-palm textures, right-hand full-palm veins, and left-hand full-palm veins. Then, the outputs are fused to provide the final recognition decision. Two datasets are exploited; both are from the Chinese Academy of Sciences Institute of Automation's (CASIA) Multi-Spectral Palmprint Image Database (version. 1.0), where full-palm images of the two spectra 460nm and 940nm are obtained. High performances are achieved after applying the suggested approach