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Sensor-Based Behavioral Biometrics

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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 7429

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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

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Keywords

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

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Published Papers (5 papers)

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Research

18 pages, 1672 KiB

Open AccessArticle

Pedestrian Re-Identification Based on Fine-Grained Feature Learning and Fusion

by Anming Chen and Weiqiang Liu

Sensors 2024, 24(23), 7536; https://doi.org/10.3390/s24237536 - 26 Nov 2024

Viewed by 499

Abstract

Video-based pedestrian re-identification (Re-ID) is used to re-identify the same person across different camera views. One of the key problems is to learn an effective representation for the pedestrian from video. However, it is difficult to learn an effective representation from one single modality of a feature due to complicated issues with video, such as background, occlusion, and blurred scenes. Therefore, there are some studies on fusing multimodal features for video-based pedestrian Re-ID. However, most of these works fuse features at the global level, which is not effective in reflecting fine-grained and complementary information. Therefore, the improvement in performance is limited. To obtain a more effective representation, we propose to learn fine-grained features from different modalities of the video, and then they are aligned and fused at the fine-grained level to capture rich semantic information. As a result, a multimodal token-learning and alignment model (MTLA) is proposed to re-identify pedestrians across camera videos. An MTLA consists of three modules, i.e., a multimodal feature encoder, token-based cross-modal alignment, and correlation-aware fusion. Firstly, the multimodal feature encoder is used to extract the multimodal features from the visual appearance and gait information views, and then fine-grained tokens are learned and denoised from these features. Then, the token-based cross-modal alignment module is used to align the multimodal features at the token level to capture fine-grained semantic information. Finally, the correlation-aware fusion module is used to fuse the multimodal token features by learning the inter- and intra-modal correlation, in which the features refine each other and a unified representation is obtained for pedestrian Re-ID. To evaluate the performance of fine-grained features alignment and fusion, we conduct extensive experiments on three benchmark datasets. Compared with the state-of-art approaches, all the evaluation metrices of mAP and Rank-K are improved by more than 0.4 percentage points. Full article

(This article belongs to the Special Issue Sensor-Based Behavioral Biometrics)

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13 pages, 1585 KiB

Open AccessArticle

Analyzing Arabic Handwriting Style through Hand Kinematics

by Vahan Babushkin, Haneen Alsuradi, Muhamed Osman Al-Khalil and Mohamad Eid

Sensors 2024, 24(19), 6357; https://doi.org/10.3390/s24196357 - 30 Sep 2024

Cited by 1 | Viewed by 1111

Abstract

Handwriting style is an important aspect affecting the quality of handwriting. Adhering to one style is crucial for languages that follow cursive orthography and possess multiple handwriting styles, such as Arabic. The majority of available studies analyze Arabic handwriting style from static documents, focusing only on pure styles. In this study, we analyze handwriting samples with mixed styles, pure styles (Ruq’ah and Naskh), and samples without a specific style from dynamic features of the stylus and hand kinematics. We propose a model for classifying handwritten samples into four classes based on adherence to style. The stylus and hand kinematics data were collected from 50 participants who were writing an Arabic text containing all 28 letters and covering most Arabic orthography. The parameter search was conducted to find the best hyperparameters for the model, the optimal sliding window length, and the overlap. The proposed model for style classification achieves an accuracy of 88%. The explainability analysis with Shapley values revealed that hand speed, pressure, and pen slant are among the top 12 important features, with other features contributing nearly equally to style classification. Finally, we explore which features are important for Arabic handwriting style detection. Full article

(This article belongs to the Special Issue Sensor-Based Behavioral Biometrics)

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(a) Experiment setup, (b) sample text dictated to subjects, (c) user interface with a sample of subject’s handwriting (text is shown using the Naskh style). Full article ">Figure 2
Distribution of the expert’s evaluation of style consistency by paragraphs. (a) Original distribution, (b) after retaining paragraphs that correspond to the prevailing style of the subject. Full article ">Figure 3
The proposed architecture with two temporal convolution layers: w—the length of the window, s—overlap, n—number of windows, T—length of the entire paragraph, <math display="inline"><semantics> <msub> <mi>K</mi> <mn>1</mn> </msub> </semantics></math>—kernel sizes of the first/second 1D-CNN layers, <math display="inline"><semantics> <msub> <mi>C</mi> <mn>1</mn> </msub> </semantics></math>—number of channels in first/second 1D-CNN layers. Full article ">Figure 4
Searching for optimal (a) overlap and (b) window size. Full article ">Figure 5
Average of confusion matrices over 5 folds. The diagonal shows average recall across 5 folds for each of the 4 classes. Full article ">Figure 6
Average of normalized Shapley values across 5 folds. Full article ">

14 pages, 761 KiB

Open AccessArticle

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 837

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 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

Open AccessArticle

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 2 | Viewed by 2020

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 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

Open AccessArticle

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 2 | Viewed by 2037

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 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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