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Biometrics frameworks have essentially enhanced individual authentication, playing a
significant part in personal, national, and global security. Existing ocular biometric
system achieves good accuracy results for images acquired using NIR cameras in ideal
condition only. When visible wavelength images are acquired in unconstrained
environment, noise is introduced such as illumination, reflection, motion blur etc.
which degrade the recognition performance. This research presents a multimodal eye
biometric framework utilizing Support value-based fusion (SVBF) matching process to
enhance biometric authentication by combining the of iris, sclera, and pupil
characteristics from unconstrained coloured eye images. A multimodal biometric
architecture using the fusion-associating support-value method is introduced in this
report to improve biometric authentication. The proposed strategy is portrayed in
subsequent steps; initially CNN (Convolutions Neural Network) segmentation based
on quality feature selection using entropy is applied to cluster iris, pupils and sclera
region. Subsequently effective features are extracted from the segmented iris, pupil and
sclera region, for example colour histogram, Log Gabor and sclera Y- shape features.
On the basis of the extricated features, the support value-based fusion is determined,
and the matching score is calculated by means of the minimum, maximum value and
support value derived from the features. Finally, authentic person is predictable by
computing a Euclidean distance of training and testing matching scores. The proposed
findings are tested on constrained database MMU, unconstrained image database
UBIRIS.V2 and mobile image database MICHE to show with the current techniques
the efficiency of the proposed authentication technique. Experimental results shows
that proposed multimodal biometric system provides better results as compared to
existing state-of-art. Segmentation performed using E-CNN improves results for
segmentation accuracy up to 97.99% for iris, 98.08% for sclera and 99.43% for pupil
segmentation under uncontrolled environment by reducing segmentation time up to
0.9sec. Proposed SVBF framework also highlights the role of feature level fusion to
enhance the recognition accuracy up to 97% for unconstrained visible wavelength
images.
