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Advances in Sensor-Based Biometric Recognition
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Advances in Sensor-Based Biometric Recognition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 3313

Special Issue Editors

Dr. Yichao Yan

E-Mail Website
Guest Editor
AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: neural rendering; domain adaptation/generalization; person re-identification; image/video generation; action recognition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jie Qin

E-Mail Website
Guest Editor
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: computer vision; machine learning; pattern recognition; image/video processing; human activity analysis; person re-identification; content-based image/video retrieval; zero-shot learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biometric recognition based on physical attributes, e.g., fingerprints, facial images, iris, gait, palm prints, and voice, is a fundamental task in computer vision and pattern recognition. Recently, significant progress has been made to boost the accuracy of biometric recognition due to the fast development of biometric sensors, e.g., optical, capacitive, ultrasonic, thermal, and pressure sensors. This Special Issue aims to solicit original research from both industry and academia on recent advances, solutions, applications, and new challenges in the field of sensor-based biometric recognition. The topics of interest include (but are not limited to) the following areas:

  • Challenges in sensor-based biometric recognition
  • Robust capturing systems for biometric recognition
  • Multi-sensor feature fusion for biometric recognition
  • Novel benchmarks for biometric recognition
  • Generalizable capturing system for sensor-based biometric recognition
  • Multi-modal biometric recognition
  • Sensors in mobile devices for biometric recognition
  • Non-contact sensor-based biometric recognition
  • Surveys/reviews for sensor-based biometric recognition

Dr. Yichao Yan
Prof. Dr. Jie Qin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (1 paper)

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Research

18 pages, 1470 KiB  
Article
Facial Expression Recognition Using Local Sliding Window Attention
by Shuang Qiu, Guangzhe Zhao, Xiao Li and Xueping Wang
Sensors 2023, 23(7), 3424; https://doi.org/10.3390/s23073424 - 24 Mar 2023
Cited by 8 | Viewed by 2715
Abstract
There are problems associated with facial expression recognition (FER), such as facial occlusion and head pose variations. These two problems lead to incomplete facial information in images, making feature extraction extremely difficult. Most current methods use prior knowledge or fixed-size patches to perform [...] Read more.
There are problems associated with facial expression recognition (FER), such as facial occlusion and head pose variations. These two problems lead to incomplete facial information in images, making feature extraction extremely difficult. Most current methods use prior knowledge or fixed-size patches to perform local cropping, thereby enhancing the ability to acquire fine-grained features. However, the former requires extra data processing work and is prone to errors; the latter destroys the integrity of local features. In this paper, we propose a local Sliding Window Attention Network (SWA-Net) for FER. Specifically, we propose a sliding window strategy for feature-level cropping, which preserves the integrity of local features and does not require complex preprocessing. Moreover, the local feature enhancement module mines fine-grained features with intraclass semantics through a multiscale depth network. The adaptive local feature selection module is introduced to prompt the model to find more essential local features. Extensive experiments demonstrate that our SWA-Net model achieves a comparable performance to that of state-of-the-art methods with scores of 90.03% on RAF-DB, 89.22% on FERPlus, 63.97% on AffectNet. Full article
(This article belongs to the Special Issue Advances in Sensor-Based Biometric Recognition)
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