IET Biometrics最新文献

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.

Gait recognition uses video of human gait processed by computer vision methods to identify people based on walking style. The complexity introduced by covariates makes the previous methods less efficient and inaccurate. This study proposes an approach based on pose features to attempt gait recognition of people with an overcoat, carrying objects, or other covariates. It aims to estimate human locomotion using Convolutional Neural Networks. Gathering video data, extracting video frames in a particular order, posture estimation for each frame, using multilayer RNN for gait recognition from the pose, and obtaining one-dimensional object vectors, are all critical steps. Furthermore, these one-dimensional identification vectors are stored in a data set along with the name of the person walking in the video. The proposed data set is used to train a classification model to predict the person in a new video by first processing it to get its identification vector and then to use it as a test case in the classification model. A graphical user interface was also developed so that anyone with no programming or technical experience can easily use the tool. The developed application does everything for gait detection from mp4 videos by obtaining the identification vectors and saving them into the data set. Using this application, one can quickly identify the person walking in a video. The results obtained offered an accuracy from 60.88% to 95.23%.

This special issue of IET Biometrics, “BIOSIG 2021 Special Issue on Efficient, Reliable, and Privacy-Friendly Biometrics”, has as starting point the 2021 edition of the Biometric Special Interest Group (BIOSIG) conference. This special issue gathers works focussing on topics of biometric recognition put under the new light of fostering the efficiency, reliability and privacy of biometrics systems and methods.

The “BIOSIG 2021 Special Issue on Efficient, Reliable, and Privacy-Friendly Biometrics” issue contains 12 papers, several of them being extended versions of papers presented at the BIOSIG 2021 conference, dealing with concrete research areas within biometrics such as Presentation Attack Detection for Face and Iris, Biometric Template Protection Schemes and Deep Learning techniques for Biometrics.

Paper “Face Morphing Attacks and Face Image Quality: The Effect of Morphing and the Attack Detectability by Quality” was authored by Biying Fu and Naser Damer. This paper addresses the effect of morphing processes both on the perceptual image quality and the image utility in face recognition (FR) when compared to bona fide samples. This work provides an extensive analysis of the effect of morphing on face image quality, including both general image quality measures and face image utility measures, analysing six different morphing techniques and five different data sources using 10 different quality measures. The consistent separability between the quality scores of morphing attack and bona fide samples measured by certain quality measures sustains the proposal of performing unsupervised morphing attack detection (MAD) based on quality scores. The study looks into intra- and inter-dataset detectability to evaluate the generalisability of such a detection concept on different morphing techniques and bona fide sources. The results obtained point out that a set of quality measures, such as MagFace and CNNNIQA, can be used to perform unsupervised and generalised MAD with a correct classification accuracy of over 70%.

Paper “Pixel-Wise Supervision for Presentation Attack Detection on ID Cards” was authored by Raghavendra Mudgalgundurao, Patrick Schuch, Kiran Raja, Raghavendra Ramachandra, and Naser Damer. This paper addresses the problem of detection of fake ID cards that are printed and then digitally presented for biometric authentication purposes in unsupervised settings. The authors propose a method based on pixel-wise supervision, using DenseNet, to leverage minute cues on various artefacts such as moiré patterns and artefacts left by the printers. To test the proposed system, a new database was obtained from an operational system, consisting of 886 users with 433 bona fide, 67 print and 366 display attacks (not publicly available due to GPDR regulations). The proposed approach achieves better performance compared to handcrafted features and deep learning models, with an Equal Error Rate (EER) of 2.22% and Bo

With the continuous progress and development in the field of Internet technology, the area of medical image processing has also developed along with it. Specially, digital watermarking technology plays an essential role in the field of medical image processing and greatly improves the security of medical image information. A medical image watermarking algorithm based on an accelerated-KAZE discrete cosine transform (AKAZE-DCT) is proposed to address the poor robustness of medical image watermarking algorithms to geometric attacks, which leads to low security of the information contained in medical images. First, the AKAZE-DCT algorithm is used to extract the feature vector of the medical image and then combined with the perceptual hashing technique to obtain the feature sequence of the medical image; then, the watermarking image is encrypted with logistic chaos dislocation to get the encrypted watermarking image, which ensures the security of the watermarking information; finally, the watermarking is embedded and extracted with the zero-watermarking technique. The experimental results show that the algorithm can effectively extract the watermark under conventional and geometric attacks, reflecting better robustness and invisibility, and has certain practicality in the medical field compared with other algorithms.