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Applied Sciences
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Applications of Image Processing and Pattern Recognition in Biometrics
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Applications of Image Processing and Pattern Recognition in Biometrics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 November 2024 | Viewed by 5664

Special Issue Editors

Dr. Elias N. Zois

E-Mail Website
Guest Editor
Department of Electrical and Electronics Engineering, University of West Attica, 12243 Athens, Greece
Interests: digital signal; image processing; computer vision; pattern recognition; handwriting biometry
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dimitrios Kalivas

E-Mail Website
Guest Editor
Department of Electrical and Electronics Engineering, University of West Attica, 12243 Athens, Greece
Interests: Image processing; video processing; pattern recognition; image and video communications

Special Issue Information

Dear Colleagues,

We would like to invite you to submit your research work to our Special Issue, “Applications of Image Processing and Pattern Recognition in Biometrics”.

This Special Issue focuses upon the interdisciplinary processing, extraction of information, and recognition of all types of biometric content contained in digital images, with important applications in high-level understanding. Biometrics is the task of identifying individuals with the use of their physiological or behavioral traits. A number of relative scientific disciplines describes this topic; among others acquisition, image analysis, machine learning, computer vision are some notable examples. This Special Issue aims to bring together and present recent advances in theory, comprehensive studies, and surveys, as well as contemporary and new research ideas in order to process and understand the biometric content of images as a whole or as a part of a combined infrastructure. Submissions should provide progress and advance the state-of-the-art at image processing, computer vision, and pattern recognition/machine learning fields with a primary focus on physiological and behavioral biometric attributes.

Broad topics/keywords and areas of interest include, but are not limited to:

  • Individual biometric modalities, including established and traditional, contemporary modalities, as well as any type of their fusion.
  • Hardware technologies for biometric image processing and pattern recognition.
  • Core groundwork theory and applications on image processing, pattern recognition, machine learning, and computer vision techniques relevant to biometrics processing and analysis.
  • Datasets, evaluation, and benchmarking.
  • Image processing, pattern recognition, machine learning, and computer vision for biometrics in healthcare, banking, IOT, etc.
  • Image processing, pattern recognition, machine learning, and computer vision for biometrics in forensic applications.

Dr. Elias N. Zois
Prof. Dr. Dimitrios Kalivas
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. Applied Sciences 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 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.

Published Papers (4 papers)

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Research

14 pages, 473 KiB  
Article
Speaker Anonymization: Disentangling Speaker Features from Pre-Trained Speech Embeddings for Voice Conversion
by Marco Matassoni, Seraphina Fong and Alessio Brutti
Appl. Sci. 2024, 14(9), 3876; https://doi.org/10.3390/app14093876 (registering DOI) - 30 Apr 2024
Viewed by 174
Abstract
Speech is a crucial source of personal information, and the risk of attackers using such information increases day by day. Speaker privacy protection is crucial, and various approaches have been proposed to hide the speaker’s identity. One approach is voice anonymization, which aims [...] Read more.
Speech is a crucial source of personal information, and the risk of attackers using such information increases day by day. Speaker privacy protection is crucial, and various approaches have been proposed to hide the speaker’s identity. One approach is voice anonymization, which aims to safeguard speaker identity while maintaining speech content through techniques such as voice conversion or spectral feature alteration. The significance of voice anonymization has grown due to the necessity to protect personal information in applications such as voice assistants, authentication, and customer support. Building upon the S3PRL-VC toolkit and on pre-trained speech and speaker representation models, this paper introduces a feature disentanglement approach to improve the de-identification performance of the state-of-the-art anonymization approaches based on voice conversion. The proposed approach achieves state-of-the-art speaker de-identification and causes minimal impact on the intelligibility of the signal after conversion. Full article
(This article belongs to the Special Issue Applications of Image Processing and Pattern Recognition in Biometrics)
19 pages, 3467 KiB  
Article
A Medical Image Encryption Scheme for Secure Fingerprint-Based Authenticated Transmission
by Francesco Castro, Donato Impedovo and Giuseppe Pirlo
Appl. Sci. 2023, 13(10), 6099; https://doi.org/10.3390/app13106099 - 16 May 2023
Cited by 11 | Viewed by 2102
Abstract
Secure transmission of medical images and medical data is essential in healthcare systems, both in telemedicine and AI approaches. The compromise of images and medical data could affect patient privacy and the accuracy of diagnosis. Digital watermarking embeds medical images into a non-significant [...] Read more.
Secure transmission of medical images and medical data is essential in healthcare systems, both in telemedicine and AI approaches. The compromise of images and medical data could affect patient privacy and the accuracy of diagnosis. Digital watermarking embeds medical images into a non-significant image before transmission to ensure visual security. However, it is vulnerable to white-box attacks because the embedded medical image can be extracted by an attacker that knows the system’s operation and does not ensure the authenticity of image transmission. A visually secure image encryption scheme for secure fingerprint-based authenticated transmission has been proposed to solve the above issues. The proposed scheme embeds the encrypted medical image, the encrypted physician’s fingerprint, and the patient health record (EHR) into a non-significant image to ensure integrity, authenticity, and confidentiality during the medical image and medical data transmission. A chaotic encryption algorithm based on a permutation key has been used to encrypt the medical image and fingerprint feature vector. A hybrid asymmetric cryptography scheme based on Elliptic Curve Cryptography (ECC) and AES has been implemented to protect the permutation key. Simulations and comparative analysis show that the proposed scheme achieves higher visual security of the encrypted image and higher medical image reconstruction quality than other secure image encryption approaches. Full article
(This article belongs to the Special Issue Applications of Image Processing and Pattern Recognition in Biometrics)
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24 pages, 4172 KiB  
Article
Smartwatch In-Air Signature Time Sequence Three-Dimensional Static Restoration Classification Based on Multiple Convolutional Neural Networks
by Yuheng Guo and Hiroyuki Sato
Appl. Sci. 2023, 13(6), 3958; https://doi.org/10.3390/app13063958 - 20 Mar 2023
Cited by 1 | Viewed by 1076
Abstract
In-air signatures are promising applications that have been investigated extensively in the past decades; an in-air signature involves gathering datasets through portable devices, such as smartwatches. During the signing process, individuals wear smartwatches on their wrists and sign their names in the air. [...] Read more.
In-air signatures are promising applications that have been investigated extensively in the past decades; an in-air signature involves gathering datasets through portable devices, such as smartwatches. During the signing process, individuals wear smartwatches on their wrists and sign their names in the air. The dataset we used in this study collected in-air signatures from 22 participants, resulting in a total of 440 smartwatch in-air signature signals. The dynamic time warping (DTW) algorithm was applied to verify the usability of the dataset. This paper analyzes and compares the performances of multiple convolutional neural networks (CNN) and the transformer using median-sized smartwatch in-air signatures. For the four CNN models, the in-air digital signature data were first transformed into visible three-dimensional static signatures. For the transformer, the nine-dimensional in-air signature signals were concatenated and downsampled to the desired length and then fed into the transformer for time sequence signal multi-classification. The performance of each model on the smartwatch in-air signature dataset was thoroughly tested with respect to 10 optimizers and different learning rates. The best testing performance score in our experiment was 99.8514% with ResNet by using the Adagrad optimizer under a 1×10−4 learning rate. Full article
(This article belongs to the Special Issue Applications of Image Processing and Pattern Recognition in Biometrics)
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13 pages, 3716 KiB  
Article
Finger Vein and Inner Knuckle Print Recognition Based on Multilevel Feature Fusion Network
by Li Jiang, Xianghuan Liu, Haixia Wang and Dongdong Zhao
Appl. Sci. 2022, 12(21), 11182; https://doi.org/10.3390/app122111182 - 04 Nov 2022
Cited by 4 | Viewed by 1282
Abstract
Multimodal biometric recognition involves two critical issues: feature representation and multimodal fusion. Traditional feature representation requires complex image preprocessing and different feature-extraction methods for different modalities. Moreover, the multimodal fusion methods used in previous work simply splice the features of different modalities, resulting [...] Read more.
Multimodal biometric recognition involves two critical issues: feature representation and multimodal fusion. Traditional feature representation requires complex image preprocessing and different feature-extraction methods for different modalities. Moreover, the multimodal fusion methods used in previous work simply splice the features of different modalities, resulting in an unsatisfactory feature representation. To address these two problems, we propose a Dual-Branch-Net based recognition method with finger vein (FV) and inner knuckle print (IKP). The method combines convolutional neural network (CNN), transfer learning, and triplet loss function to complete feature representation, thereby simplifying and unifying the feature-extraction process of the two modalities. Dual-Branch-Net also achieves deep multilevel fusion of the two modalities’ features. We assess our method on a public FV and IKP homologous multimodal dataset named PolyU-DB. Experimental results show that the proposed method performs best and achieves an equal error rate (EER) of the recognition result of 0.422%. Full article
(This article belongs to the Special Issue Applications of Image Processing and Pattern Recognition in Biometrics)
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