IET Biometrics最新文献

{"title":"Heartbeat information prediction based on transformer model using millimetre-wave radar","authors":"Bojun Hu,&nbsp;Biao Jin,&nbsp;Hao Xue,&nbsp;Zhenkai Zhang,&nbsp;Zhaoyang Xu,&nbsp;Xiaohua Zhu","doi":"10.1049/bme2.12116","DOIUrl":"https://doi.org/10.1049/bme2.12116","url":null,"abstract":"<p>Millimetre-wave radar offers high ranging accuracy and can capture subtle vibration information of the human heart. This study proposes a heartbeat prediction method based on the transformer model using millimetre-wave radar. Firstly, the millimetre-wave radar was used to collect the heartbeat data and conduct normalisation processing. Secondly, a position coding was introduced to assign sine or cosine variables to input data and extract their relative position relationship. Subsequently, the transformer encoder was adopted to allocate attention to input data through the multi-head attention mechanism, using a mask layer before the decoding layer to prevent the leakage of future information. Finally, we employ the fully connected layer was employed in the linear decoder for regression and output the predicted results. Our experimental results demonstrate that the proposed transformer model achieves nearly 30% higher prediction accuracy than traditional long short-term memory models while improving both the prediction accuracy and convergence rate. The proposed method has great potential in predicting the heartbeat state of elderly and sick patients.</p>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"12 4","pages":"235-243"},"PeriodicalIF":2.0,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12116","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50141756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"APPSO-NN: An adaptive-probability particle swarm optimization neural network for sensorineural hearing loss detection","authors":"Jingyuan Yang,&nbsp;Yu-Dong Zhang","doi":"10.1049/bme2.12114","DOIUrl":"https://doi.org/10.1049/bme2.12114","url":null,"abstract":"<p>As a hearing disorder, sensorineural hearing loss (SNHL) can be effectively detected by magnetic resonance imaging (MRI). However, the manual detection of MRI scanning is subjective, time-consuming, and unpredictable. An accurate and automatic computer-aided diagnosis system is proposed for SNHL detection, providing reliable references for professionals. The system first employs a wavelet entropy layer to extract features of MRI images. Then, a neural network layer is proposed as the classifier consisting of a feedforward neural network (FNN) and an adaptive-probability PSO (APPSO) algorithm. The authors prove the rotation-variant property of the basic particle swarm optimization (PSO) by the algebraic property of matrix transformation. The property is unsuitable for optimising parameters of neural networks. Thus, in APPSO, the authors integrate the new update rules based on all-dimensional variation and adaptive-probability mechanism into the basic PSO, which can improve its searching ability without losing population diversity. The authors compare APPSO-NN with FNN trained by five popular evolutionary algorithms. The simulation results show that APPSO performs best in training FNN. The method also compares with six state-of-the-art methods. The simulation results show that the best performance in sensitivity and overall accuracy of hearing loss classification, which proves that the method is effective and promising for SNHL detection.</p>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"12 4","pages":"211-221"},"PeriodicalIF":2.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12114","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50133500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Turning waste into wealth: Person identification by emotion-disturbed electrocardiogram","authors":"Wei Li,&nbsp;Cheng Fang,&nbsp;Zhihao Zhu,&nbsp;Chuyi Chen,&nbsp;Aiguo Song","doi":"10.1049/bme2.12112","DOIUrl":"https://doi.org/10.1049/bme2.12112","url":null,"abstract":"<p>The issue of electrocardiogram (ECG)-based person identification has attracted intense research interests nowadays. Different than existing related researches that advocate accentuating useful information and attenuating noisy artefacts in sensor data processing, A novel strategy of ‘turning waste into wealth’ is proposed to exploit the new discriminative information from the relationship between noise disturbance and signal data for this issue. Specifically, the authors design a new and simple method, the Set-Group Distance Measure, based on the suitable fusion of multiple minority-based distance measurements, whose power has initially been discovered for the issue. This method takes advantage of the collaborative variation information from the relative relationship, which is named as ‘relative information’, between different types of emotional noise disturbances and ECG signal data, to tackle the problem of large intra-class variation but small inter-class difference during identification. Experimental results have demonstrated the reasonability, effectiveness, robustness, efficiency and practicability of the proposed method upon public benchmark databases. This proposal not only provides technological inspirations for the further study in ECG-based person identification, but also shows a fresh feasible way to handle the noise-signal relationship for more general topics of sensor data classification.</p>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"12 3","pages":"159-175"},"PeriodicalIF":2.0,"publicationDate":"2023-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12112","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50145969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An image-based facial acupoint detection approach using high-resolution network and attention fusion","authors":"Tingting Zhang,&nbsp;Hongyu Yang,&nbsp;Wenyi Ge,&nbsp;Yi Lin","doi":"10.1049/bme2.12113","DOIUrl":"https://doi.org/10.1049/bme2.12113","url":null,"abstract":"<p>With the prevalence of Traditional Chinese Medicine (TCM), automation techniques are highly required to support the therapy and save human resources. As the fundamental of the TCM treatment, acupoint detection is attracting research attention in both academic and industrial domains, while current approaches suffer from poor accuracy even with sparse acupoints or require extra equipment. In this study, considering the decision-making knowledge of human experts, an image-based deep learning approach is proposed to detect facial acupoints by localising the centre of acupoints. In the proposed approach, high-resolution networks are selected as the backbone to learn informative facial features with different resolution paths. To fuse the learnt features from the high-resolution network, a resolution, channel, and spatial attention-based fusion module is innovatively proposed to imitate human decision, that is, focusing on the facial features to detect required acupoints. Finally, the heatmap is designed to integrally achieve the acupoint classification and position localisation in a single step. A small-scale real-world dataset is constructed and annotated to evaluate the proposed approach based on the authorised face dataset. The experimental results demonstrate the proposed approach outperforms other baseline models, achieving a 2.4228% normalised mean error. Most importantly, the effectiveness and efficiency of the proposed technical improvements are also confirmed by extensive experiments. The authors believe that the proposed approach can achieve acupoint detection with considerable high performance, and further support TCM automation.</p>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"12 3","pages":"146-158"},"PeriodicalIF":2.0,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12113","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50151457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brainprint based on functional connectivity and asymmetry indices of brain regions: A case study of biometric person identification with non-expensive electroencephalogram headsets","authors":"Jordan Ortega-Rodríguez,&nbsp;Kevin Martín-Chinea,&nbsp;José Francisco Gómez-González,&nbsp;Ernesto Pereda","doi":"10.1049/bme2.12097","DOIUrl":"https://doi.org/10.1049/bme2.12097","url":null,"abstract":"<p>Brain-computer interface applications for biometric person identification have increased their interest in recent years since they are potentially more secure and more difficult to counterfeit than traditional biometric techniques. However, it is necessary to consider how brain waves are acquired for this purpose, not only in terms of efficiency but also of practical comfort for the user and the affordability degree of the biosignal acquisition device so that their everyday application can become a realistic possibility. In this context, this paper presents the capabilities of using a non-expensive wireless electroencephalogram (EEG) device to extract spectral-related and functional connectivity information of brain activity. The proposed method achieved a sufficient biometric identification with two datasets of 13 and 109 subjects when comparing the performance of a sizeable classification algorithm set. In addition, a novel feature in EEG biometric identification, called asymmetry index, is introduced here. Furthermore, this is the first study in this field to consider the effect of the time-lapse between different recording sessions on the system's behaviour when using a low-cost EEG device with identification accuracy rates of up to 100%.</p>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"12 3","pages":"129-145"},"PeriodicalIF":2.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50135592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Facilitating free travel in the Schengen area—A position paper by the European Association for Biometrics","authors":"Christoph Busch,&nbsp;Farzin Deravi,&nbsp;Dinusha Frings,&nbsp;Els Kindt,&nbsp;Ralph Lessmann,&nbsp;Alexander Nouak,&nbsp;Jean Salomon,&nbsp;Mateus Achcar,&nbsp;Fernando Alonso-Fernandez,&nbsp;Daniel Bachenheimer,&nbsp;David Bethell,&nbsp;Josef Bigun,&nbsp;Matthew Brawley,&nbsp;Guido Brockmann,&nbsp;Enrique Cabello,&nbsp;Patrizio Campisi,&nbsp;Aleksandrs Cepilovs,&nbsp;Miles Clee,&nbsp;Mickey Cohen,&nbsp;Christian Croll,&nbsp;Andrzej Czyżewski,&nbsp;Bernadette Dorizzi,&nbsp;Martin Drahansky,&nbsp;Pawel Drozdowski,&nbsp;Catherine Fankhauser,&nbsp;Julian Fierrez,&nbsp;Marta Gomez-Barrero,&nbsp;Georg Hasse,&nbsp;Richard Guest,&nbsp;Ekaterina Komleva,&nbsp;Sebastien Marcel,&nbsp;Gian Luca Marcialis,&nbsp;Laurent Mercier,&nbsp;Emilio Mordini,&nbsp;Stefance Mouille,&nbsp;Pavlina Navratilova,&nbsp;Javier Ortega-Garcia,&nbsp;Dijana Petrovska,&nbsp;Norman Poh,&nbsp;Istvan Racz,&nbsp;Ramachandra Raghavendra,&nbsp;Christian Rathgeb,&nbsp;Christophe Remillet,&nbsp;Uwe Seidel,&nbsp;Luuk Spreeuwers,&nbsp;Brage Strand,&nbsp;Sirra Toivonen,&nbsp;Andreas Uhl","doi":"10.1049/bme2.12107","DOIUrl":"https://doi.org/10.1049/bme2.12107","url":null,"abstract":"<p>Due to migration, terror-threats and the viral pandemic, various EU member states have re-established internal border control or even closed their borders. European Association for Biometrics (EAB), a non-profit organisation, solicited the views of its members on ways which biometric technologies and services may be used to help with re-establishing open borders within the Schengen area while at the same time mitigating any adverse effects. From the responses received, this position paper was composed to identify ideas to re-establish free travel between the member states in the Schengen area. The paper covers the contending needs for security, open borders and fundamental rights as well as legal constraints that any technological solution must consider. A range of specific technologies for direct biometric recognition alongside complementary measures are outlined. The interrelated issues of ethical and societal considerations are also highlighted. Provided a holistic approach is adopted, it may be possible to reach a more optimal trade-off with regards to open borders while maintaining a high-level of security and protection of fundamental rights. European Association for Biometrics and its members can play an important role in fostering a shared understanding of security and mobility challenges and their solutions.</p>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"12 2","pages":"112-128"},"PeriodicalIF":2.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50132006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of non-suicidal self-injury based on spatiotemporal features of indoor activities","authors":"Guanci Yang,&nbsp;Siyuan Yang,&nbsp;Kexin Luo,&nbsp;Shangen Lan,&nbsp;Ling He,&nbsp;Yang Li","doi":"10.1049/bme2.12110","DOIUrl":"https://doi.org/10.1049/bme2.12110","url":null,"abstract":"<p>Non-suicide self-injury (NSSI) can be dangerous and difficult for guardians or caregivers to detect in time. NSSI refers to when people hurt themselves even though they have no wish to cause critical or long-lasting hurt. To timely identify and effectively prevent NSSI in order to reduce the suicide rates of patients with a potential suicide risk, the detection of NSSI based on the spatiotemporal features of indoor activities is proposed. Firstly, an NSSI behaviour dataset is provided, and it includes four categories that can be used for scientific research on NSSI evaluation. Secondly, an NSSI detection algorithm based on the spatiotemporal features of indoor activities (NssiDetection) is proposed. NssiDetection calculates the human bounding box by using an object detection model and employs a behaviour detection model to extract the temporal and spatial features of NSSI behaviour. Thirdly, the optimal combination schemes of NssiDetection is investigated by checking its performance with different behaviour detection methods and training strategies. Lastly, a case study is performed by implementing an NSSI behaviour detection prototype system. The prototype system has a recognition accuracy of 84.18% for NSSI actions with new backgrounds, persons, or camera angles.</p>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"12 2","pages":"91-101"},"PeriodicalIF":2.0,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12110","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50130927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient ear alignment using a two-stack hourglass network","authors":"Anja Hrovatič,&nbsp;Peter Peer,&nbsp;Vitomir Štruc,&nbsp;Žiga Emeršič","doi":"10.1049/bme2.12109","DOIUrl":"https://doi.org/10.1049/bme2.12109","url":null,"abstract":"<p>Ear images have been shown to be a reliable modality for biometric recognition with desirable characteristics, such as high universality, distinctiveness, measurability and permanence. While a considerable amount of research has been directed towards ear recognition techniques, the problem of ear alignment is still under-explored in the open literature. Nonetheless, accurate alignment of ear images, especially in unconstrained acquisition scenarios, where the ear appearance is expected to vary widely due to pose and view point variations, is critical for the performance of all downstream tasks, including ear recognition. Here, the authors address this problem and present a framework for ear alignment that relies on a two-step procedure: (i) automatic landmark detection and (ii) fiducial point alignment. For the first (landmark detection) step, the authors implement and train a Two-Stack Hourglass model (2-SHGNet) capable of accurately predicting 55 landmarks on diverse ear images captured in uncontrolled conditions. For the second (alignment) step, the authors use the Random Sample Consensus (RANSAC) algorithm to align the estimated landmark/fiducial points with a pre-defined ear shape (i.e. a collection of average ear landmark positions). The authors evaluate the proposed framework in comprehensive experiments on the AWEx and ITWE datasets and show that the 2-SHGNet model leads to more accurate landmark predictions than competing state-of-the-art models from the literature. Furthermore, the authors also demonstrate that the alignment step significantly improves recognition accuracy with ear images from unconstrained environments compared to unaligned imagery.</p>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"12 2","pages":"77-90"},"PeriodicalIF":2.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12109","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50150490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adversarial liveness detector: Leveraging adversarial perturbations in fingerprint liveness detection","authors":"Antonio Galli,&nbsp;Michela Gravina,&nbsp;Stefano Marrone,&nbsp;Domenico Mattiello,&nbsp;Carlo Sansone","doi":"10.1049/bme2.12106","DOIUrl":"https://doi.org/10.1049/bme2.12106","url":null,"abstract":"<p>The widespread use of fingerprint authentication systems (FASs) in consumer electronics opens for the development of advanced presentation attacks, that is, procedures designed to bypass a FAS using a forged fingerprint. As a consequence, FAS are often equipped with a fingerprint presentation attack detection (FPAD) module, to recognise live fingerprints from fake replicas. In this work, a novel FPAD approach based on Convolutional Neural Networks (CNNs) and on an ad hoc adversarial data augmentation strategy designed to iteratively increase the considered detector robustness is proposed. In particular, the concept of adversarial fingerprint, that is, fake fingerprints disguised by using ad hoc fingerprint adversarial perturbation algorithms was leveraged to help the detector focus only on salient portions of the fingerprints. The procedure can be adapted to different CNNs, adversarial fingerprint algorithms and fingerprint scanners, making the proposed approach versatile and easily customisable todifferent working scenarios. To test the effectiveness of the proposed approach, the authors took part in the LivDet 2021 competition, an international challenge gathering experts to compete on fingerprint liveness detection under different scanners and fake replica generation approach, achieving first place out of 23 participants in the ‘Liveness Detection in Action track’.</p>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"12 2","pages":"102-111"},"PeriodicalIF":2.0,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50127461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal feature-algorithm combination research for EEG fatigue driving detection based on functional brain network","authors":"Yi Zhou,&nbsp;ChangQing Zeng,&nbsp;ZhenDong Mu","doi":"10.1049/bme2.12108","DOIUrl":"https://doi.org/10.1049/bme2.12108","url":null,"abstract":"<p>With the increasing number of motor vehicles globally, the casualties and property losses caused by traffic accidents are substantial worldwide. Traffic accidents caused by fatigue driving are also increasing year by year. In this article, the authors propose a functional brain network-based driving fatigue detection method and seek to combine features and algorithms with optimal effect. First, a simulated driving experiment is established to obtain EEG signal data from multiple subjects in a long-term monotonic cognitive task. Second, the correlation between each EEG signal channel is calculated using Pearson correlation coefficient to construct a functional brain network. Then, five functional brain network features (clustering coefficient, node degree, eccentricity, local efficiency, and characteristic path length) are extracted and combined to obtain a total of 26 features and eight machine learning algorithms (SVM, LR, DT, RF, KNN, LDA, ADB, GBM) are used as classifiers for fatigue detection respectively. Finally, the optimal combination of features and algorithms are obtained. The results show that the feature combination of node degree, local efficiency, and characteristic path length achieves the best classification accuracy of 92.92% in the logistic regression algorithm.</p>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"12 2","pages":"65-76"},"PeriodicalIF":2.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50138604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}