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Symmetry
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Symmetry Applied in Biometrics Technology
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Symmetry Applied in Biometrics Technology

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 9187

Special Issue Editor

Prof. Dr. Jules-Raymond Tapamo

E-Mail Website
Guest Editor
Professor of Computer Science and Engineering, University of KwaZulu-Natal, Durban, South Africa
Interests: image processing; computer vision; machine learning; biometrics; data science

Special Issue Information

Dear Colleagues,

Advancements in sensor technology and computational power have created opportunites to build systems that mimic some human abilities. Biometric technology has immensely benefited from these developments. There have been several applications in areas, such as border control, banking, access control, etc., that use biometric modalities, such as the iris, face, fingerprint, palm print, ear, etc. The symmetrical nature of many biometric features has been exploited in some existing works to improve the accuracy of biometric systems. This gives research directions for using inner information of the ears  in recognition systems. This Special Issue focuses on the application of symmetry-based biometric solutions. It encourages researchers to submit state-of-the-art theoretical and/or application-based findings, with the use of symmetry to create new biometric-based systems. as well as  models or/and improve existing ones. You are welcome to submit original research or review articles.

Prof. Dr. Jules-Raymond Tapamo
Guest Editor

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. Symmetry is an international peer-reviewed open access monthly 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 2400 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.

Keywords

  • biometrics
  • identification
  • symmetry
  • pattern recognition
  • soft biometrics
  • safety
  • security

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

25 pages, 15271 KiB  
Article
Symmetry Alignment–Feature Interaction Network for Human Ear Similarity Detection and Authentication
by Li Yuan, He-Bin Zhou, Jiang-Yun Li, Li Liu, Xiao-Chai Gu and Ya-Nan Zhao
Symmetry 2025, 17(5), 654; https://doi.org/10.3390/sym17050654 (registering DOI) - 26 Apr 2025
Viewed by 69
Abstract
In the context of ear-based biometric identity authentication, symmetry between the left and right ears emerges as a pivotal factor, particularly when registration involves one ear and authentication utilizes its contralateral counterpart. The extent to which bilateral ear symmetry supports consistent identity verification [...] Read more.
In the context of ear-based biometric identity authentication, symmetry between the left and right ears emerges as a pivotal factor, particularly when registration involves one ear and authentication utilizes its contralateral counterpart. The extent to which bilateral ear symmetry supports consistent identity verification warrants significant investigation. This study addresses this challenge by proposing a novel framework, the Symmetry Alignment–Feature Interaction Network, designed to enhance authentication robustness. The proposed network incorporates a Symmetry Alignment Module, leveraging differentiable geometric alignment and a dual-attention mechanism to achieve precise feature correspondence between the left and right ears, thereby mitigating the robustness deficiencies of conventional methods under pose variations. Additionally, a Feature Interaction Network is introduced to amplify nonlinear interdependencies between binaural features, employing a difference–product dual-path architecture to enhance feature discriminability through Dual-Path Feature Interaction and Similarity Fusion. Experimental validation on a dataset from the University of Science and Technology of Beijing demonstrates that the proposed method achieves a similarity detection accuracy of 99.03% (a 9.11% improvement over the baseline ResNet18) and an F1 score of 0.9252 in identity authentication tasks. Ablation experiments further confirm the efficacy of the Symmetry Alignment Module, reducing the false positive rate by 3.05%, in combination with the Feature Interaction Network, shrinking the standard deviation of similarity distributions between the positive and negative samples by 67%. A multi-task loss function, governed by a dynamic weighting mechanism, effectively balances feature learning objectives. This work establishes a new paradigm for the authentication of biometric features with symmetry, integrating symmetry modeling with Dual-Path Feature Interaction and Similarity Fusion to advance the precision of ear authentication. Full article
(This article belongs to the Special Issue Symmetry Applied in Biometrics Technology)
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14 pages, 2032 KiB  
Article
Peak Detection and HRV Feature Evaluation on ECG and PPG Signals
by Filipa Esgalhado, Arnaldo Batista, Valentina Vassilenko, Sara Russo and Manuel Ortigueira
Symmetry 2022, 14(6), 1139; https://doi.org/10.3390/sym14061139 - 1 Jun 2022
Cited by 25 | Viewed by 8302
Abstract
Heart Rate Variability (HRV) evaluates the autonomic nervous system regulation and can be used as a monitoring tool in conditions such as cardiovascular diseases, neuropathies and sleep staging. It can be extracted from the electrocardiogram (ECG) and the photoplethysmogram (PPG) signals. Typically, the [...] Read more.
Heart Rate Variability (HRV) evaluates the autonomic nervous system regulation and can be used as a monitoring tool in conditions such as cardiovascular diseases, neuropathies and sleep staging. It can be extracted from the electrocardiogram (ECG) and the photoplethysmogram (PPG) signals. Typically, the HRV is obtained from the ECG processing. Being the PPG sensor widely used in clinical setups for physiological parameters monitoring such as blood oxygenation and ventilatory rate, the question arises regarding the PPG adequacy for HRV extraction. There is not a consensus regarding the PPG being able to replace the ECG in the HRV estimation. This work aims to be a contribution to this research area by comparing the HRV estimation obtained from simultaneously acquired ECG and PPG signals from forty subjects. A peak detection method is herein introduced based on the Hilbert transform: Hilbert Double Envelope Method (HDEM). Two other peak detector methods were also evaluated: Pan-Tompkins and Wavelet-based. HRV parameters for time, frequency and the non-linear domain were calculated for each algorithm and the Pearson correlation, T-test and RMSE were evaluated. The HDEM algorithm showed the best overall results with a sensitivity of 99.07% and 99.45% for the ECG and the PPG signals, respectively. For this algorithm, a high correlation and no significant differences were found between HRV features and the gold standard, for the ECG and PPG signals. The results show that the PPG is a suitable alternative to the ECG for HRV feature extraction. Full article
(This article belongs to the Special Issue Symmetry Applied in Biometrics Technology)
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