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’.

The proposed schema: (a) the model is fine‐tuned on the clean challenge data. Adversarial fingerprints are crafted based on it; (b) the model is further fine‐tuned by using the new dataset consisting of both the original and perturbed fingerprints; and (c) the model is fine‐tuned for the last time by using the new dataset consisting of both original and perturbed fingerprints.image

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.

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.image

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.

Optimal feature‐algorithm combination research for EEG fatigue driving detection based on functional brain network.image

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.

Ear alignment using two‐stack hourglass network for landmark detection and Random Sample Consensus for fiducial point alignment. Aligned ear images are compared against unaligned in recognition experiments using two widely used ear datasets and multiple approaches.image

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.

The detection of NSSI based on the spatiotemporal features of indoor activities (NssiDetection) is proposed. NssiDetection is characterised by the use of temporal and spatial features to detect the NSSI behaviour. We investigate the optimal combination schemes of NssiDetection by checking its performance with different behaviour detection methods and training strategies. We perform a case study by implementing an NSSI behaviour detection prototype system, which shows a recognition accuracy of 84.18% for NSSI action with new backgrounds, persons or camera angles.image