Pose invariant palm vein identification system using convolutional neural network

Other Title(s)

نظام تحديد الهوية من خلال أوردة اليد الثابتة الموضع باستخدام الشبكة العصبية التلافيفية

Joint Authors

Hasan, Nida Fulayyih
Abd al-Razzaq, Husam Imad

Source

Baghdad Science Journal

Issue

Vol. 15, Issue 4 (31 Dec. 2018), pp.503-510, 8 p.

Publisher

University of Baghdad College of Science for Women

Publication Date

2018-12-31

Country of Publication

Iraq

No. of Pages

8

Main Subjects

Natural & Life Sciences (Multidisciplinary)

Abstract EN

Palm vein recognition is a one of the most efficient biometric technologies, each individual can be identified through its veins unique characteristics, palm vein acquisition techniques is either contact based or contactless based, as the individual's hand contact or not the peg of the palm imaging device, the needs a contactless palm vein system in modern applications rise tow problems, the pose variations (rotation, scaling and translation transformations) since the imaging device cannot aligned correctly with the surface of the palm, and a delay of matching process especially for large systems, trying to solve these problems.

This paper proposed a pose invariant identification system for contactless palm vein which include three main steps, at first data augmentation is done by making multiple copies of the input image then perform out-of-plane rotation on them around all the X,Y and Z axes.

Then a new fast extract Region of Interest (ROI) algorithm is proposed for cropping palm region.

Finally, features are extracted and classified by specific structure of Convolutional Neural Network (CNN).

The system is tested on two public multispectral palm vein databases (PolyU and CASIA); furthermore, synthetic datasets are derived from these mentioned databases, to simulate the hand out-of-plane rotation in random angels within range from -20° to +20° degrees.

To study several situations of pose invariant, twelve experiments are performed on all datasets, highest accuracy achieved is 99.73% ∓ 0.27 on PolyU datasets and 98 % ∓ 1 on CASIA datasets, with very fast identification process, about 0.01 second for identifying an individual, which proves system efficiency in contactless palm vein problems

American Psychological Association (APA)

Hasan, Nida Fulayyih& Abd al-Razzaq, Husam Imad. 2018. Pose invariant palm vein identification system using convolutional neural network. Baghdad Science Journal،Vol. 15, no. 4, pp.503-510.
https://search.emarefa.net/detail/BIM-866055

Modern Language Association (MLA)

Hasan, Nida Fulayyih& Abd al-Razzaq, Husam Imad. Pose invariant palm vein identification system using convolutional neural network. Baghdad Science Journal Vol. 15, no. 4 (2018), pp.503-510.
https://search.emarefa.net/detail/BIM-866055

American Medical Association (AMA)

Hasan, Nida Fulayyih& Abd al-Razzaq, Husam Imad. Pose invariant palm vein identification system using convolutional neural network. Baghdad Science Journal. 2018. Vol. 15, no. 4, pp.503-510.
https://search.emarefa.net/detail/BIM-866055

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references : p. 509-510

Record ID

BIM-866055