  The current developments in the field of machine vision have opened new
vistas towards deploying multimodal biometric recognition systems in various
real-world applications. These systems have the ability to deal with the
limitations of unimodal biometric systems which are vulnerable to spoofing,
noise, non-universality and intra-class variations. In addition, the ocular
traits among various biometric traits are preferably used in these recognition
systems. Such systems possess high distinctiveness, permanence, and performance
while, technologies based on other biometric traits (fingerprints, voice etc.)
can be easily compromised. This work presents a novel deep learning framework
called SIP-SegNet, which performs the joint semantic segmentation of ocular
traits (sclera, iris and pupil) in unconstrained scenarios with greater
accuracy. The acquired images under these scenarios exhibit purkinje reflexes,
specular reflections, eye gaze, off-angle shots, low resolution, and various
occlusions particularly by eyelids and eyelashes. To address these issues,
SIP-SegNet begins with denoising the pristine image using denoising
convolutional neural network (DnCNN), followed by reflection removal and image
enhancement based on contrast limited adaptive histogram equalization (CLAHE).
Our proposed framework then extracts the periocular information using adaptive
thresholding and employs the fuzzy filtering technique to suppress this
information. Finally, the semantic segmentation of sclera, iris and pupil is
achieved using the densely connected fully convolutional encoder-decoder
network. We used five CASIA datasets to evaluate the performance of SIP-SegNet
based on various evaluation metrics. The simulation results validate the
optimal segmentation of the proposed SIP-SegNet, with the mean f1 scores of
93.35, 95.11 and 96.69 for the sclera, iris and pupil classes respectively.
