GazeNet : a lightweight multitask sclera feature extractor

Vitek, Matej (author), Štruc, Vitomir (author), Peer, Peter (author)

The sclera is a recently emergent biometric modality with many desirable characteristics. However, most literature solutions for sclera-based recognition rely on sequences of complex deep networks with significant computational overhead. In this paper, we propose a lightweight multitask-based sclera feature extractor. The proposed GazeNet network has a computational complexity below 1 GFLOP, making it appropriate for less capable devices like smartphones and head-mounted displays. Our experiments show that GazeNet (which is based on the SqueezeNet architecture) outperforms both the base SqueezeNet model as well as the more computationally intensive ScleraNET model from the literature. Thus, we demonstrate that our proposed gaze-direction multitask learning procedure, along with careful lightweight architecture selection, leads to computationally efficient networks with high recognition performance.

• Language: English
• Keywords: biometrics, ocular biometrics, sclera recognition, lightweight, feature extraction
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: Str. 661-671
• Numbering: Vol. 112
• PID: 20.500.12556/RUL-165074
• UDC: 004.93:57.087.1
• ISSN on article: 1110-0168
• DOI: 10.1016/j.aej.2024.11.011
• COBISS.SI-ID: 215847939
• Publication date in RUL: 22.11.2024

Record is a part of a journal

• Title: Alexandria Engineering Journal
• Shortened title: Alex. Eng. J.
• Publisher: Elsevier
• ISSN: 1110-0168
• COBISS.SI-ID: 6305307

Licences

• License: CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
• Link: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Description: The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.

Secondary language

• Language: Slovenian
• Keywords: biometrija, očesna biometrija, razpoznava beločnice, lahki modeli, luščenje značilk