23-05-2012, 01:33 PM
NEW PALM PRINT AUTHENTICATION SYSTEM BY USING WAVELET BASED METHOD
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INTRODUCTION:
Biometrics based personal identification is getting wide acceptance in the networked society,
replacing passwords and keys due to its reliability, uniqueness and the ever in-creasing demand of
security. Common modalities being used are fingerprint and face but for face authentication
people are still working with the problem of pose and illumination invariance where as fingerprint
does not have a good psychological effect on the user because of its wide use in crime
investigations. If any biometric modality is to succeed in the future it should have the traits like
unique-ness, accuracy, richness, ease of acquisition, reliability and above all user acceptance.
Palm print based personal identification is a new biometric modality which is getting wide
acceptance and has all the necessary traits to make it a part of our daily life. This paper
investigates the use of palm print for personal identification using combination of dif-ferment
wavelets.
DEVELOPMENT OF IMAGE ACQUISITION PLAT-FORM:
There are two types of systems available for capturing the palm print of individuals i.e., scanners
and the pegged systems [16], [17]. Scanners are hygienically not safe whereas the pegged systems
cause considerable inconvenience to the user. Hence both of these systems suffer from low user
acceptability. The attributes of ease of acquisition and hygienic safety are of paramount
importance for any biometric modality.
IMAGE REGISTRATION:
Our image registration approach follows the technique proposed in “[13]”, and is summarized as
follows: The ac-quirked color (RGB) parameters of palm print are changed to HSI parameters.
The hue value of skin is same so it was safely neglected along with the less discriminating
saturation value. The palm print has been analyzed for its texture using the gray level or intensity
values, I among the HSI values. Gray level images retain all the useful discriminating information
required for personal identification, along with considerable reduction in processing time the
color images are changed into gray level images using following equation:
FEATURE EXTRACTION AND CLASSIFICATION:
We obtained ten images of each individual of which five were used for training and the rest of
them were used for validation. The obtained registered palm print image has been analyzed for its
texture using different symmetrical wavelet families namely biorthogonal 3.9, symmelt 8 and
demeyer 5.