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Applications of Image Processing and Pattern Recognition in Biometrics

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

Deadline for manuscript submissions: 10 June 2025 | Viewed by 11742

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Special Issue Editors

Dr. Elias N. Zois

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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.

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Published Papers (6 papers)

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Research

31 pages, 4101 KiB

Open AccessArticle

Fingerprint Classification Based on Multilayer Extreme Learning Machines

by Axel Quinteros and David Zabala-Blanco

Appl. Sci. 2025, 15(5), 2793; https://doi.org/10.3390/app15052793 - 5 Mar 2025

Viewed by 198

Abstract

Fingerprint recognition is one of the most effective and widely adopted methods for person identification. However, the computational time required for the querying of large databases is excessive. To address this, preprocessing steps such as classification are necessary to speed up the response time to a query. Fingerprints are typically categorized into five classes, though this classification is unbalanced. While advanced classification algorithms, including support vector machines (SVMs), multilayer perceptrons (MLPs), and convolutional neural networks (CNNs), have demonstrated near-perfect accuracy (approaching 100%), their high training times limit their widespread applicability across institutions. In this study, we introduce, for the first time, the use of a multilayer extreme learning machine (M-ELM) for fingerprint classification, aiming to improve training efficiency. A comparative analysis is conducted with CNNs and unbalanced extreme learning machines (W-ELMs), as these represent the most influential methodologies in the literature. The tests utilize a database generated by SFINGE software, which simulates realistic fingerprint distributions, with datasets comprising hundreds of thousands of samples. To optimize and simplify the M-ELM, widely recognized descriptors in the field—Capelli02, Liu10, and Hong08—are used as input features. This effectively reduces dimensionality while preserving the representativeness of the fingerprint information. A brute-force heuristic optimization approach is applied to determine the hyperparameters that maximize classification accuracy across different M-ELM configurations while avoiding excessive training times. A comparison is made with the aforementioned approaches in terms of accuracy, penetration rate, and computational cost. The results demonstrate that a two-layer hidden ELM achieves superior classification of both majority and minority fingerprint classes with remarkable computational efficiency. Full article

(This article belongs to the Special Issue Applications of Image Processing and Pattern Recognition in Biometrics)

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20 pages, 3791 KiB

Open AccessArticle

Impact of Texture Feature Count on the Accuracy of Osteoporotic Change Detection in Computed Tomography Images of Trabecular Bone Tissue

by Róża Dzierżak

Appl. Sci. 2025, 15(3), 1528; https://doi.org/10.3390/app15031528 - 2 Feb 2025

Viewed by 751

Abstract

The aim of this study is to compare the classification accuracy depending on the number of texture features used. This study used 400 computed tomography (CT) images of trabecular spinal tissue from 100 patients belonging to two groups (50 control patients and 50 patients diagnosed with osteoporosis). The descriptors of texture features were based on a gray level histogram, gradient matrix, RL matrix, event matrix, an autoregressive model, and wavelet transformation. From the 290 obtained texture features, the features with fixed values were eliminated and structured according to the feature importance ranking. The classification performance was assessed using 267, 200, 150, 100, 50, 20, and 10 texture features to build classifiers. The classifiers applied in this study included Naive Bayes, Multilayer Perceptron, Hoeffding Tree, K-nearest neighbors, and Random Forest. The following indicators were used to assess the quality of the classifiers: accuracy, sensitivity, specificity, precision, negative predictive value, Matthews correlation coefficient, and F1 score. The highest performance was achieved by the K-Nearest Neighbors (K = 1) and Multilayer Perceptron classifiers. KNN demonstrated the best results with 50 features, attaining a highest F1 score of 96.79% and accuracy (ACC) of 96.75%. MLP achieved its optimal performance with 100 features, reaching an accuracy and F1 score of 96.50%. This demonstrates that building a classifier using a larger number of features, without a selection process, allows us to achieve high classification effectiveness and holds significant diagnostic value. Full article

(This article belongs to the Special Issue Applications of Image Processing and Pattern Recognition in Biometrics)

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Figure 1
Illustration of the selection of cancellous bone tissue area for the study (red box). Full article ">Figure 2
Imaging samples of tissue from healthy patients and tissue with osteoporotic changes in their original size. Full article ">Figure 3
(a) Visualization of the distribution of feature values: (a) the first in the ranking, (b) the last in the ranking. red—values for osteoporosis tissue, blue—values for healthy tissue. Full article ">Figure 4
Changes in the values of ACC, TPR, and TNR parameters depending on the number of features for Naive Bayes classification. Full article ">Figure 5
Changes in the values of ACC, TPR, and TNR parameters depending on the number of features for Multilayer Perceptron classification. Full article ">Figure 6
Changes in the values of ACC, TPR, and TNR parameters depending on the number of features for Hoeffding Tree classification. Full article ">Figure 7
Changes in the values of ACC, TPR, and TNR parameters depending on the number of features for K_NN classification. Full article ">Figure 8
Changes in the values of ACC, TPR, and TNR parameters depending on the number of features for Random Forest classifications. Full article ">Figure 9
Confusion matrices for (a) K-Nearest Neighbors and (b) Multilayer Perceptron classifiers. Full article ">

14 pages, 470 KiB

Open AccessArticle

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 - 30 Apr 2024

Cited by 2 | Viewed by 1866

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 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)

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19 pages, 3467 KiB

Open AccessArticle

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 20 | Viewed by 3408

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 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

Open AccessArticle

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 1811

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. 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 \times 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

Open AccessArticle

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 - 4 Nov 2022

Cited by 8 | Viewed by 2108

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 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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