Fully Convolutional Networks and Generative Adversarial Networks Applied to Sclera Segmentation

TL;DR

This paper explores the application of Fully Convolutional Networks and Generative Adversarial Networks for sclera segmentation in biometric recognition, providing new datasets and achieving high accuracy on two public databases.

Contribution

Introduces FCN and GAN-based methods for sclera segmentation, along with a new dataset of 1,300 manually segmented images for evaluation.

Findings

Achieved F-score of 87.48% on UBIRIS.v2 database.

Achieved F-score of 88.32% on MICHE database.

Provided a new dataset for sclera segmentation research.

Abstract

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.