Learning to combine local and global image information for contactless palmprint recognition

Stoimčev, Marjan (author), Ivanovska, Marija (author), Štruc, Vitomir (author)

In the past few years, there has been a leap from traditional palmprint recognition methodologies, which use handcrafted features, to deep-learning approaches that are able to automatically learn feature representations from the input data. However, the information that is extracted from such deep-learning models typically corresponds to the global image appearance, where only the most discriminative cues from the input image are considered. This characteristic is especially problematic when data is acquired in unconstrained settings, as in the case of contactless palmprint recognition systems, where visual artifacts caused by elastic deformations of the palmar surface are typically present in spatially local parts of the captured images. In this study we address the problem of elastic deformations by introducing a new approach to contactless palmprint recognition based on a novel CNN model, designed as a two-path architecture, where one path processes the input in a holistic manner, while the second path extracts local information from smaller image patches sampled from the input image. As elastic deformations can be assumed to most significantly affect the global appearance, while having a lesser impact on spatially local image areas, the local processing path addresses the issues related to elastic deformations thereby supplementing the information from the global processing path. The model is trained with a learning objective that combines the Additive Angular Margin (ArcFace) Loss and the well-known center loss. By using the proposed model design, the discriminative power of the learned image representation is significantly enhanced compared to standard holistic models, which, as we show in the experimental section, leads to state-of-the-art performance for contactless palmprint recognition. Our approach is tested on two publicly available contactless palmprint datasets—namely, IITD and CASIA—and is demonstrated to perform favorably against state-of-the-art methods from the literature. The source code for the proposed model is made publicly available.

• Language: English
• Keywords: palmprint recognition, contactless palmprint images, elastic deformations, convolutional neural networks, deep learning, ArcFace loss, center loss, discriminative feature learning
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FE - Faculty of Electrical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2022
• Number of pages: 26 str.
• Numbering: Vol. 22, iss. 1, art. 73
• PID: 20.500.12556/RUL-136908
• UDC: 004.93
• ISSN on article: 1424-8220
• DOI: 10.3390/s22010073
• COBISS.SI-ID: 90907395
• Publication date in RUL: 24.05.2022

Record is a part of a journal

• Title: Sensors
• Shortened title: Sensors
• Publisher: MDPI
• ISSN: 1424-8220
• COBISS.SI-ID: 10176278

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 01.01.2022

Secondary language

• Language: Slovenian
• Keywords: samodejno razpoznavanje dlani, brezstične slike dlani, elastične deformacije, konvolucijske nevronske mreže, globoko učenje, središčna izgubna funkcija, diskriminativno luščenje značilk

Projects

• Funder: ARRS - Slovenian Research Agency
• Project number: P2-0250
• Name: Metrologija in biometrični sistemi