A palmprint identification system using robust discriminant orientation code

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A palmprint identification system using robust discriminant orientation code. This paper presents a palmprint recognition system in which we propose a novel acquisition device and a Robust Discriminant Orientation Code, called RDORIC, for palmprint identification. In order to get the clear line features, the device is designed to capture the palmprint images under Green illuminations.
VNU Journal of Science: Comp. Science & Com. Eng., Vol. 30, No. 4 (2014) 1-13
A Palmprint Identification System Using Robust
Discriminant Orientation Code
Hoang Thien Van1, Thai Hoang Le2
1Department of Computer Sciences, Ho Chi Minh City University of Technology, Vietnam
2Department of Computer Sciences, Ho Chi Minh University of Science, Vietnam
Abstract
This paper presents a palmprint recognition system in which we propose a novel acquisition device and a Robust
Discriminant Orientation Code, called RDORIC, for palmprint identification. In order to get the clear line
features, the device is designed to capture the palmprint images under Green illuminations. To extract RDORIC
feature, we present the algorithm which includes two main steps: (1) Palm line orientation map computation and
(2) Discriminant feature extraction of the orientation map. In the first step, positive orientation and negative
orientation maps are computed by applying the modified finite Radon transform (MFRAT). In the second step,
the grid-sampling based 2DLDA, called Grid-LDA, is used to remove redundant information of orientation maps
and form a class-separable code more suitable for palmprint identification. The experimental results on the
database of our lab and the public database of Hong Kong Polytechnic University (PolyU) show that our
technique provides a very robust orientation representation for recognition and demonstrate the feasibility of the
proposed system.
© 2014 Published by VNU Journal of Science.
Manuscript communication: received 15 December 2013, revised 13 April 2014, accepted 13 May 2014
Corresponding author: Hoang Thien Van, vthoang@hcmhutech.edu.vn
Keywords: Palmprint identification; Modified Finite Radon Transform; 2DLDA, GridLDA; DORIR.
1. Introduction
coordinate
system
to
align
palmprint
images
Palmprint is a new kind of biometric feature
for personal recognition and has been widely
studied due to its merits such as distinctiveness,
cost-effectiveness, user friendliness, high
accuracy, and so on [1]. Palmprint research
employs low resolution images (i.e., less than
150 dpi, see Fig. 1a) for civil and commercial
applications. A typical palmprint system
consists of five parts: data acquisition device,
region of interest (ROI) extraction, feature
extraction, matcher and database. The data
acquisition device collects palmprint images
(see Fig. 1c). ROI extraction sets up a
and to segment a part of palmprint images for
feature extraction (see Fig. 1b). Feature
extraction obtains effective features from the
ROI images. A matcher compares two
palmprint features and a database stores
registered templates. Feature extraction is an
important step of palmprint recognition.
Palmprint features are principal lines and
wrinkles, called palm-lines, which are very
important to distinguish between different
palmprints and can be extracted from low-
resolution images. There are many approaches
exploiting palm lines for recognition such as:
line-based approaches, code-based approaches,