IET Biometrics最新文献

We are at the wake of an epochal revolution, the Information Revolution. The Information Revolution has been accompanied by the rise of a new commodity, digital data, which is changing the world including methods for human recognition. Biometric systems are the recognition technology of the new age. So, privacy scholars tend to frame biometric privacy protection chiefly in terms of biometric data protection. The author argues that this is a misleading perspective. Biometric data protection is an extremely relevant legal and commercial issue but has little to do with privacy. The notion of privacy, understood as a personal intimate sphere, is hardly related to what is contained in this private realm (data or whatever else), rather it is related to the very existence of a secluded space. Privacy relies on having the possibility to hide rather than in hiding anything. What really matters is the existence of a private sphere rather than what is inside. This also holds true for biometric privacy. Biometric privacy protection should focus on bodily and psychological integrity, preventing those technology conditions and operating practices that may lead to turn biometric recognition into a humiliating experience for the individual.

The exponential growth in the use of smartphones means that users must constantly be concerned about the security and privacy of mobile data because the loss of a mobile device could compromise personal information. To address this issue, continuous authentication systems have been proposed, in which users are monitored transparently after initial access to the smartphone. In this study, the authors address the problem of user authentication by considering human activities as behavioural biometric information. The authors convert the behavioural biometric data (considered as time series) into a 2D colour image. This transformation process keeps all the characteristics of the behavioural signal. Time series does not receive any filtering operation with this transformation, and the method is reversible. This signal-to-image transformation allows us to use the 2D convolutional networks to build efficient deep feature vectors. This allows them to compare these feature vectors to the reference template vectors to compute the performance metric. The authors evaluate the performance of the authentication system in terms of Equal Error Rate on a benchmark University of Californy, Irvine Human Activity Recognition dataset, and they show the efficiency of the approach.

Improving interoperability in contactless-to-contact fingerprint matching is a crucial factor for the mainstream adoption of contactless fingerphoto devices. However, matching contactless probe images against legacy contact-based gallery images is very challenging due to the presence of heterogeneity between these domains. Moreover, unconstrained acquisition of fingerphotos produces perspective distortion. Therefore, direct matching of fingerprint features suffers severe performance degradation on cross-domain interoperability. In this study, to address this issue, the authors propose a coupled adversarial learning framework to learn a fingerprint representation in a low-dimensional subspace that is discriminative and domain-invariant in nature. In fact, using a conditional coupled generative adversarial network, the authors project both the contactless and the contact-based fingerprint into a latent subspace to explore the hidden relationship between them using class-specific contrastive loss and ArcFace loss. The ArcFace loss ensures intra-class compactness and inter-class separability, whereas the contrastive loss minimises the distance between the subspaces for the same finger. Experiments on four challenging datasets demonstrate that our proposed model outperforms state-of-the methods and two top-performing commercial-off-the-shelf SDKs, that is, Verifinger v12.0 and Innovatrics. In addition, the authors also introduce a multi-finger score fusion network that significantly boosts interoperability by effectively utilising the multi-finger input of the same subject for both cross-domain and cross-sensor settings.

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.

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.

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.

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.

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%.