IET Biometrics最新文献

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%.

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.

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.

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.

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’.

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.

Activity classification and biometric authentication have become synonymous with wearable technologies such as smartwatches and trackers. Although great efforts have been made to develop electrocardiogram (ECG)-based biometric verification and identification modalities using data from these devices, in this paper, we explore the use of adaptive techniques based on prior activity classification in an attempt to enhance biometric performance. In doing so, we also compare two waveform similarity distances to provide features for classification. Two public datasets which were collected from medical and wearable devices provide a cross-device comparison. Our results show that our method is able to be used for both wearable and medical devices in activity classification and biometric verification cases. This study is the first study which uses only ECG signals for both activity classification and biometric verification purposes.

Over the past few decades, biometric security is increasingly becoming an important tool to enhance security and brings greater convenience. Nowadays, biometric systems are widely used by government agencies and private industries. Though a growing effort has been devoted in order to develop robust biometric recognition systems that can operate in various conditions, many problems still remain to be solved, including the design of techniques to handle varying illumination sources, occlusions and low quality images resulting from uncontrolled acquisition conditions.

The performance of any biometric recognition system heavily depends on finding a good and suitable feature representation space satisfying, smoothness, cluster, manifold, sparsity and temporal/spatial coherence, where observations from different classes are well separated. Unfortunately, finding this proper representation is a challenging problem which has taken a huge interest in machine learning and computer vision communities.

Representation learning methods can be organised in two main groups: ‘intra-class’ and ‘inter-class’. In the first group, the techniques seek to extract useful information from the raw data itself. They broadly range from conventional hand-crafted feature design based on the human knowledge about the target application (SIFT, Local Binary Patterns, HoG, etc.), to dimensionality reduction techniques (PCA, linear discriminant analysis, Factor Analysis, isometric mapping, Locally Linear Embedding, etc.) and feature selection (wrapper, filter, embedded), until the recent deep representations which achieved state-of-the-art performances in many applications.

The ‘inter-class’ techniques seek to find a structure and relationship between the different data observations. In this group, we can find metric/kernel learning, investigating the spatial or temporal relationship among different examples, while subspace/manifold learning techniques seek to discover the underlying inherent structural property.

The objective of this special issue is to provide a stage for worldwide researchers to publish their recent and original results on representation learning for robust biometric systems. There are in total eight articles accepted for publication in this Special Issue through careful peer reviews and revisions.

Li et al. introduced a watermarking algorithm based on an accelerated-KAZE discrete cosine transform (AKAZE-DCT) to address the poor robustness of the image watermarking algorithms to geometric attacks. Firstly, the extracted features using AKAZE-DCT are combined with the perceptual hashing, then, the watermarking image is encrypted with logistic chaos dislocation, finally, the watermarking is embedded and extracted with the zero-watermarking technique. The experimental results showed that the algorithm can effectively extract the watermark under conventional and geometric attacks, reflecting better robustness and invisibility.