Light-weight deep models for sclera recognition ; Lahki globoki modeli za prepoznavo beločnice

Sclera recognition is a subfield within biometric recognition technology that focuses on identifying individuals based on the vascular structures in the sclera, i.e. the white part of the eye. Most existing solutions for sclera recognition are based either on hand-crafted methods from the field of computer vision, which perform suboptimally, or on deep convolutional networks, which require powerful hardware to run efficiently. However, biometric systems are increasingly being deployed on smartphones, head-mounted displays, and edge devices, which require light-weight models, i.e. simple computational models capable of running well on weaker hardware. As such, in our thesis (i) we propose the novel method IPAD, which decreases the number of parameters and operations in a deep network, and using IPAD we develop a light-weight model for sclera segmentation, and (ii) we develop the light-weight GazeNet network, based on the SqueezeNet architecture and trained via multi-task learning, which we use as our sclera vessel feature extractor. The results of our extensive experimental analysis affirm the superiority of deep convolutional networks over classical hand-crafted methods. On the other hand, our analysis of the models developed with the IPAD method demonstrates that the networks commonly relied on in the literature can be significantly reduced in terms of their spatial and computational requirements, without a significant decrease in accuracy -- in fact, in certain cases, simplifying the models even enhances their accuracy. Even light-weight deep networks require a significant amount of training data to achieve high-quality performance. We note that, while iris datasets are plentiful, there is a considerable lack of sclera-focused datasets. Thus, as part of the aforementioned contributions, we introduce MOBIUS, the first publicly available mobile-camera-acquired dataset intended primarily for sclera segmentation, although it can be used for iris and periocular biometrics as well. Finally, since biometric systems ...