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Sensor-Based Behavioral Biometrics
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Sensor-Based Behavioral Biometrics

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

Deadline for manuscript submissions: 10 February 2025 | Viewed by 3932

Special Issue Editors

Dr. Kiril Alexiev

E-Mail Website
Guest Editor
Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
Interests: sensor data processing; identification; data and information fusion; sensor networks; computer networks, biometrics
Dr. Virginio Cantoni

E-Mail Website
Guest Editor
Computer Engineering, University of Pavia, Pavia, Italy
Interests: computer vision; pattern recognition; image processing

Special Issue Information

Dear Colleagues,

Behavioral biometrics is a subfield of the science of personal identification. The main goal is to build a unique pattern of behavior of a certain type of activity of a person by which they can be identified. Usually, the considered activities are physical and cognitive. In the broader sense, however, biosignals can also be added as a reflection of the functioning of certain human organs. Of interest are the individual gait or the manner of walking, gesturing, speed and intonation of speaking and the manner of handling various devices and tools, such as smartphones, keyboards, computer mouse, etc. Among the cognitive ones, we can count the movement of the eyes when perceiving textual information, searching for an object in a scene, searching for mistakes or repetitions, counting certain types of objects, the way of working on the Internet, etc. In the field of biosignals, there are already developments for biometrics based on eye movement, ECG and EEG signals, human breathing, etc.

It is interesting to note that in a number of cases, information concerning individual behavior is already available (usually recorded by the digital device we work with) and only needs to be subjected to additional processing in order to make the identification.

Behavioral biometrics can be seen as a powerful additional means of identification. With the development of various methods of behavioral biometrics, it is expected that in the near future, it will find a place in almost all digital devices and helps prevent different types of fraud.

Dr. Kiril Alexiev
Dr. Virginio Cantoni
Guest Editors

Manuscript Submission Information

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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.

Keywords

  • sensors/sensing
  • biometrics
  • biometric recognition
  • biosignal
  • ECG/EEG/EMG/EOG signal sensing
  • biometric systems

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Published Papers (3 papers)

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Research

14 pages, 761 KiB  
Article
Online Signature Biometrics for Mobile Devices
by Katarzyna Roszczewska and Ewa Niewiadomska-Szynkiewicz
Sensors 2024, 24(11), 3524; https://doi.org/10.3390/s24113524 - 30 May 2024
Viewed by 518
Abstract
This paper addresses issues concerning biometric authentication based on handwritten signatures. Our research aimed to check whether a handwritten signature acquired with a mobile device can effectively verify a user’s identity. We present a novel online signature verification method using coordinates of points [...] Read more.
This paper addresses issues concerning biometric authentication based on handwritten signatures. Our research aimed to check whether a handwritten signature acquired with a mobile device can effectively verify a user’s identity. We present a novel online signature verification method using coordinates of points and pressure values at each point collected with a mobile device. Convolutional neural networks are used for signature verification. In this paper, three neural network models are investigated, i.e., two self-made light SigNet and SigNetExt models and the VGG-16 model commonly used in image processing. The convolutional neural networks aim to determine whether the acquired signature sample matches the class declared by the signer. Thus, the scenario of closed set verification is performed. The effectiveness of our method was tested on signatures acquired with mobile phones. We used the subset of the multimodal database, MobiBits, that was captured using a custom-made application and consists of samples acquired from 53 people of diverse ages. The experimental results on accurate data demonstrate that developed architectures of deep neural networks can be successfully used for online handwritten signature verification. We achieved an equal error rate (EER) of 0.63% for random forgeries and 6.66% for skilled forgeries. Full article
(This article belongs to the Special Issue Sensor-Based Behavioral Biometrics)
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35 pages, 6451 KiB  
Article
Efhamni: A Deep Learning-Based Saudi Sign Language Recognition Application
by Lama Al Khuzayem, Suha Shafi, Safia Aljahdali, Rawan Alkhamesie and Ohoud Alzamzami
Sensors 2024, 24(10), 3112; https://doi.org/10.3390/s24103112 - 14 May 2024
Cited by 1 | Viewed by 1230
Abstract
Deaf and hard-of-hearing people mainly communicate using sign language, which is a set of signs made using hand gestures combined with facial expressions to make meaningful and complete sentences. The problem that faces deaf and hard-of-hearing people is the lack of automatic tools [...] Read more.
Deaf and hard-of-hearing people mainly communicate using sign language, which is a set of signs made using hand gestures combined with facial expressions to make meaningful and complete sentences. The problem that faces deaf and hard-of-hearing people is the lack of automatic tools that translate sign languages into written or spoken text, which has led to a communication gap between them and their communities. Most state-of-the-art vision-based sign language recognition approaches focus on translating non-Arabic sign languages, with few targeting the Arabic Sign Language (ArSL) and even fewer targeting the Saudi Sign Language (SSL). This paper proposes a mobile application that helps deaf and hard-of-hearing people in Saudi Arabia to communicate efficiently with their communities. The prototype is an Android-based mobile application that applies deep learning techniques to translate isolated SSL to text and audio and includes unique features that are not available in other related applications targeting ArSL. The proposed approach, when evaluated on a comprehensive dataset, has demonstrated its effectiveness by outperforming several state-of-the-art approaches and producing results that are comparable to these approaches. Moreover, testing the prototype on several deaf and hard-of-hearing users, in addition to hearing users, proved its usefulness. In the future, we aim to improve the accuracy of the model and enrich the application with more features. Full article
(This article belongs to the Special Issue Sensor-Based Behavioral Biometrics)
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19 pages, 1537 KiB  
Article
A Perifacial EMG Acquisition System for Facial-Muscle-Movement Recognition
by Jianhang Zhang, Shucheng Huang, Jingting Li, Yan Wang, Zizhao Dong and Su-Jing Wang
Sensors 2023, 23(21), 8758; https://doi.org/10.3390/s23218758 - 27 Oct 2023
Cited by 1 | Viewed by 1535
Abstract
This paper proposes a portable wireless transmission system for the multi-channel acquisition of surface electromyography (EMG) signals. Because EMG signals have great application value in psychotherapy and human–computer interaction, this system is designed to acquire reliable, real-time facial-muscle-movement signals. Electrodes placed on the [...] Read more.
This paper proposes a portable wireless transmission system for the multi-channel acquisition of surface electromyography (EMG) signals. Because EMG signals have great application value in psychotherapy and human–computer interaction, this system is designed to acquire reliable, real-time facial-muscle-movement signals. Electrodes placed on the surface of a facial-muscle source can inhibit facial-muscle movement due to weight, size, etc., and we propose to solve this problem by placing the electrodes at the periphery of the face to acquire the signals. The multi-channel approach allows this system to detect muscle activity in 16 regions simultaneously. Wireless transmission (Wi-Fi) technology is employed to increase the flexibility of portable applications. The sampling rate is 1 KHz and the resolution is 24 bit. To verify the reliability and practicality of this system, we carried out a comparison with a commercial device and achieved a correlation coefficient of more than 70% on the comparison metrics. Next, to test the system’s utility, we placed 16 electrodes around the face for the recognition of five facial movements. Three classifiers, random forest, support vector machine (SVM) and backpropagation neural network (BPNN), were used for the recognition of the five facial movements, in which random forest proved to be practical by achieving a classification accuracy of 91.79%. It is also demonstrated that electrodes placed around the face can still achieve good recognition of facial movements, making the landing of wearable EMG signal-acquisition devices more feasible. Full article
(This article belongs to the Special Issue Sensor-Based Behavioral Biometrics)
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