  Due to the world's demand for security systems, biometrics can be seen as an
important topic of research in computer vision. One of the biometric forms that
has been gaining attention is the recognition based on sclera. The initial and
paramount step for performing this type of recognition is the segmentation of
the region of interest, i.e. the sclera. In this context, two approaches for
such task based on the Fully Convolutional Network (FCN) and on Generative
Adversarial Network (GAN) are introduced in this work. FCN is similar to a
common convolution neural network, however the fully connected layers (i.e.,
the classification layers) are removed from the end of the network and the
output is generated by combining the output of pooling layers from different
convolutional ones. The GAN is based on the game theory, where we have two
networks competing with each other to generate the best segmentation. In order
to perform fair comparison with baselines and quantitative and objective
evaluations of the proposed approaches, we provide to the scientific community
new 1,300 manually segmented images from two databases. The experiments are
performed on the UBIRIS.v2 and MICHE databases and the best performing
configurations of our propositions achieved F-score's measures of 87.48% and
88.32%, respectively.
