This study presents a robust framework that leverages advanced deep-learning techniques for ear-based human recognition. Faced with the challenge of dataset sizes, our approach is developed based on a generative adversarial network (GAN) method namely Pix2Pix to augment the dataset. It is demonstrated that this approach offers the ability to produce complementary images for ear recognition. To be more specific, Pix2Pix GAN is employed to generate missing sides in ear image pairs (i.e., creating corresponding left ear images for right ear images and vice versa). As such, this augmentation could substantially increase the dataset size, making it more diverse and of significantly greater use for training purposes. The employed dataset consisted of several images of the right ear and only one left ear for each individual. A series of corresponding synthetic left-ear images is generated using Pix2Pix GAN as a tool for augmenting the available data and mitigate the dataset’s lack of left ear images. The experiment framework used the EarNet model and conducted comparative evaluations before and after Pix2Pix GAN augmentation using the AMI Ear dataset. By employing the Pix2Pix GAN, the proposed approach can effectively double the size of a dataset and, in the process, provide significantly greater utility regarding how that data can be utilised in real-world applications scenarios. The resulting accuracy reaches 98% on the AMI dataset, demonstrating that this technique can improve model performance for ear-based human recognition.

A novel instance-based algorithm for pattern classification is presented and evaluated in this paper. This new method is motivated by the challenge of pattern classifications where only limited and/or noisy training data are available. For every classification, the proposed system transforms the query data and the training templates based on their distributions in the feature space. One of the major novelties of the proposed method is the concept of template reconstruction enabling improved performance with limited training data. The technique is compared with similar algorithms and evaluated using both the image and time-series modalities to demonstrate its effectiveness and versatility. Two public image databases, FASHION-MNIST and CIFAR-10, were used to test its effectiveness for the classification of images using small amounts of training samples. An average classification improvement of 2~3% was observed while using a small subset of the training database, compared to the performances achieved by state-of-the-art techniques using the full datasets. To further explore its capability in solving more challenging classification problems such as non-stationary time-series electroencephalography (EEG) signals, a clinical grade 64-electrode EEG database, as well as a low-quality (high-noise level) EEG database, obtained using a low-cost system equipped with a single dry sensor, have also been used to test the algorithm. Adaptive reconstruction of the feature instances has been seen to have substantially improved class separation and matching performance for both still images and time-series signals. In particular, the method is found to be effective for the classification of noisy non-stationary data with limited training data volumes, indicating its potential suitability for a wide range of applications.

The use of Biometric Facial Recognition (FR) systems have become increasingly widespread, especially since the advent of deep neural network-based architectures. Although FR systems provide substantial benefits in terms of security and safety, the use of these systems also raises significant privacy concerns. This article discusses recent advances in facial identity hiding techniques, focusing on privacy protection approaches that hide or protect facial biometric data before camera devices capture the data. Moreover, we also discuss the state-of-the-art methods used to evaluate such privacy protection techniques. The primary motivation of this survey is to assess the relative performance of facial privacy protection methods and identify open challenges and future work that needs to be considered in this research area.

Biometrics on mobile devices has attracted a lot of attention in recent years as it is considered a user-friendly authentication method. This interest has also been motivated by the success of Deep Learning (DL). Architectures based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been established to be convenient for the task, improving the performance and robustness in comparison to traditional machine learning techniques. However, some aspects must still be revisited and improved. To the best of our knowledge, this is the first article that intends to explore and propose novel gait biometric recognition systems based on Transformers, which currently obtain state-of-the-art performance in many applications. Several state-of-the-art architectures (Vanilla, Informer, Autoformer, Block-Recurrent Transformer, and THAT) are considered in the experimental framework. In addition, new configurations of the Transformers are proposed to further increase the performance. Experiments are carried out using the two popular public databases whuGAIT and OU-ISIR. The results achieved prove the high ability of the proposed Transformer, outperforming state-of-the-art CNN and RNN architectures.

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.

The intention of this position paper is to comment on the joint European Data Protection Supervisor (EDPS)-Agencia Española de Protección de Datos (aepd) publication ‘14 Misunderstandings with regard to Biometric Identification and Authentication’ that was published in June 2020 and to provide additional input to help with the better understanding of the issues raised in that publication. In particular, it aims to highlight some important missing information in the aforementioned publication. It is hoped that this paper will help with any future revision of the EDPS-aepd publication, such that it includes a full picture of the current state of the art in biometrics and the availability of standards and privacy enhancing techniques.

Despite the high performances achieved using deep learning techniques in biometric systems, the inability to rationalise the decisions reached by such approaches is a significant drawback for the usability and security requirements of many applications. For Facial Biometric Presentation Attack Detection (PAD), deep learning approaches can provide good classification results but cannot answer the questions such as "Why did the system make this decision"? To overcome this limitation, an explainable deep neural architecture for Facial Biometric Presentation Attack Detection is introduced in this paper. Both visual and verbal explanations are produced using the saliency maps from a Grad-CAM approach and the gradient from a Long-Short-Term-Memory (LSTM) network with a modified gate function. These explanations have also been used in the proposed framework as additional information to further improve the classification performance. The proposed framework utilises both spatial and temporal information to help the model focus on anomalous visual characteristics that indicate spoofing attacks. The performance of the proposed approach is evaluated using the CASIA-FA, Replay Attack, MSU-MFSD, and HKBU MARs datasets and indicates the effectiveness of the proposed method for improving performance and producing usable explanations.

In this paper, a novel re-engineering mechanism for the generation of word embeddings is proposed for document-level sentiment analysis. Current approaches to sentiment analysis often integrate feature engineering with classification, without optimizing the feature vectors explicitly. Engineering feature vectors to match the data between the training set and query sample as proposed in this paper could be a promising way for boosting the classification performance in machine learning applications. The proposed mechanism is designed to re-engineer the feature components from a set of embedding vectors for greatly increased between-class separation, hence better leveraging the informative content of the documents. The proposed mechanism was evaluated using four public benchmarking datasets for both two-way and five-way semantic classifications. The resulting embeddings have demonstrated substantially improved performance for a range of sentiment analysis tasks. Tests using all the four datasets achieved by far the best classification results compared with the state-of-the-art.

A novel instance-based method for the classification of electroencephalography (EEG) signals is presented and evaluated in this paper. The non-stationary nature of the EEG signals, coupled with the demanding task of pattern recognition with limited training data as well as the potentially noisy signal acquisition conditions, have motivated the work reported in this study. The proposed adaptive template enhancement mechanism transforms the feature-level instances by treating each feature dimension separately, hence resulting in improved class separation and better query-class matching. The proposed new instance-based learning algorithm is compared with a few related algorithms in a number of scenarios. A clinical grade 64-electrode EEG database, as well as a low-quality (high-noise level) EEG database obtained with a low-cost system using a single dry sensor have been used for evaluations in biometric person recognition. The proposed approach demonstrates significantly improved classification accuracy in both identification and verification scenarios. In particular, this new method is seen to provide a good classification performance for noisy EEG data, indicating its potential suitability for a wide range of applications.

Numerous studies in the literature have already shown the potential of biometrics on mobile devices for authentication purposes. However, it has been shown that, the learning processes associated to biometric systems might expose sensitive personal information about the subjects. This study proposes GaitPrivacyON, a novel mobile gait biometrics verification approach that provides accurate authentication results while preserving the sensitive information of the subject. It comprises two modules: i) a convolutional Autoencoder that transforms attributes of the biometric raw data, such as the gender or the activity being performed, into a new privacy-preserving representation; and ii) a mobile gait verification system based on the combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) with a Siamese architecture. The main advantage of GaitPrivacyON is that the first module (convolutional Autoencoder) is trained in an unsupervised way, without specifying the sensitive attributes of the subject to protect. The experimental results achieved using two popular databases (MotionSense and MobiAct) suggest the potential of GaitPrivacyON to significantly improve the privacy of the subject while keeping user authentication results higher than 99% Area Under the Curve (AUC). To the best of our knowledge, this is the first mobile gait verification approach that considers privacy-preserving methods trained in an unsupervised way.

The number of mobile devices, such as smartphones and smartwatches, is relentlessly increasing, to almost 6.8 billion by 2022, and along with it, the amount of personal and sensitive data captured by them. This survey overviews the state of the art of what personal and sensitive user attributes can be extracted from mobile device sensors, emphasizing critical aspects such as demographics, health and body features, activity and behavior recognition, and so forth. In addition, we review popular metrics in the literature to quantify the degree of privacy and discuss powerful privacy methods to protect the sensitive data while preserving data utility for analysis. Finally, open research questions are presented for further advancements in the field.

Facial recognition is becoming an increasingly popular way to authenticate users, helped by the increased use of biometric technology within mobile devices, such as smartphones and tablets. Biometric systems use thresholds to identify whether a user is genuine or an impostor. Traditional biometric systems are static (such as eGates at airports), which allow the operators and developers to create an environment most suited for the successful operation of the biometric technology by using a fixed threshold value to determine the authenticity of the user. However, with a mobile device and scenario, the operational conditions are beyond the control of the developers and operators.In this paper, we propose a novel approach to mobile biometric authentication within a mobile scenario, by offering an adaptive threshold to authenticate users based on the environment, situations and conditions in which they are operating the device. Utilising smartphone sensors, we demonstrate the creation of a successful scenario classification. Using this, we propose our idea of an extendable framework to allow multiple scenario thresholds. Furthermore, we test the concept with data collected from a smartphone device. Results show that using an adaptive scenario threshold approach can improve the biometric performance, and hence could allow manufacturers to produce algorithms that perform consistently in multiple scenarios without compromising security, allowing an increase in public trust towards the use of the technology.

Presentation attack artefacts can be used to subvert the operation of biometric systems by being presented to the sensors of such systems. In this work, we propose the use of visual stimuli with randomised trajectories to stimulate eye movements for the detection of such spoofing attacks. The presentation of a moving visual challenge is used to ensure that some pupillary motion is stimulated and then captured with a camera. Various types of challenge trajectories are explored on different planar geometries representing prospective devices where the challenge could be presented to users. To evaluate the system, photo, 2D mask and 3D mask attack artefacts were used and pupillary movement data were captured from 80 volunteers performing genuine and spoofing attempts. The results support the potential of the proposed features for the detection of biometric presentation attacks.

The objective of this study is to evaluate and compare the performance of a set of low-cost multi-modal head tracking systems incorporating facial gestures as a switching mechanism. The proposed systems are aimed to enable severely disabled patients to access a computer. In this paper, we are comparing RGB (2D) and RGB-D (3D) sensors for both head tracking and facial gesture recognition. System evaluations and usability assessment were carried out on 21 healthy individuals. Two types of head tracking systems were compared - a web camera-based and another using the Kinect sensor. The two facial switching mechanisms were eye blink and eyebrows movement. Fitts’ Test is used to evaluate the proposed systems. Movement Time (MT) was used to rank the performance of the proposed systems. The Kinect-Eyebrows system had the lowest MT, followed by the Kinect-Blink, Webcam-Blink and Webcam-Eyebrows systems. The 3D Kinect systems performed better than the 2D Vision systems for both gestures. Both Kinect systems have the lowest MT and best performance, thus showing the advantage of using depth.

Magnetoencephalography (MEG) has been combined with machine learning techniques, to recognize the Alzheimer's disease (AD), one of the most common forms of dementia. However, most of the previous studies are limited to binary classification and do not fully utilize the two available MEG modalities (extracted using magnetometer and gradiometer sensors). AD consists of several stages of progression, this study addresses this limitation by using both magnetometer and gradiometer data to discriminate between participants with AD, AD-related mild cognitive impairment (MCI), and healthy control (HC) participants in the form of a three-class classification problem. A series of wavelet-based biomarkers are developed and evaluated, which concurrently leverage the spatial, frequency and time domain characteristics of the signal. A bimodal recognition system based on an improved score-level fusion approach is proposed to reinforce interpretation of the brain activity captured by magnetometers and gradiometers. In this preliminary study, it was found that the markers derived from gradiometer tend to outperform the magnetometer-based markers. Interestingly, out of the total 10 regions of interest, left-frontal lobe demonstrates about 8% higher mean recognition rate than the second-best performing region (left temporal lobe) for AD/MCI/HC classification. Among the four types of markers proposed in this work, the spatial marker developed using wavelet coefficients provided the best recognition performance for the three-way classification. Overall, the proposed approach provides promising results for the potential of AD/MCI/HC three-way classification utilizing the bimodal MEG data.

Over the past few years, there has been an exponential increase in the percentage of people owning and using a smart phone. These devices have sensor-rich touchscreens that can capture sensitive biometric features such as keystroke typing and finger-swiping patterns. Touch-dynamics based behavioural biometrics is a time-based assessment of how a user performs a particular touch task on a mobile device. Several performance-focused surveys already exist. In this article, building upon the existing reviews, we have examined studies on touch-dynamics based behavioural biometrics based on usability and its impact on authentication performance. We also emphasize the need for shifting the focus on usability during performance evaluations by presenting a consolidated list of usability and ergonomic-based factors that influence user interaction and cause performance variations. In this article, we report and review the usability evaluations: user acceptance studies and performance-based studies influencing the user interaction process on three specific touch-dynamics based modalities—signature, keystroke, and swipe. With regards to performance, we present a comparative analysis of error rates and accuracy of various research works undertaken. Additionally, we present a consolidated list of public datasets and discuss evolving vulnerabilities of touch-dynamics based behavioural biometrics, their adopted attack models, and their feasibility. Finally, we present our assessment of this domain's existing unresolved problems that could pave the way for future research.

Studies on developing effective neuromarkers based on magnetoencephalographic (MEG) signals have been drawing increasing attention in the neuroscience community. This study explores the idea of using source-based magnitude-squared spectral coherence as a spatial indicator for effective regions of interest (ROIs) localization, subsequently discriminating the participants with mild cognitive impairment (MCI) from a group of age-matched healthy control (HC) elderly participants. We found that the cortical regions could be divided into two distinctive groups based on their coherence indices. Compared to HC, some ROIs showed increased connectivity (hyper-connected ROIs) for MCI participants, whereas the remaining ROIs demonstrated reduced connectivity (hypo-connected ROIs). Based on these findings, a series of wavelet-based source-level neuromarkers for MCI detection are proposed and explored, with respect to the two distinctive ROI groups. It was found that the neuromarkers extracted from the hyper-connected ROIs performed significantly better for MCI detection than those from the hypo-connected ROIs. The neuromarkers were classified using support vector machine (SVM) and k-NN classifiers and evaluated through Monte Carlo cross-validation. An average recognition rate of 93.83% was obtained using source-reconstructed signals from the hyper-connected ROI group. To better conform to clinical practice settings, a leave-one-out cross-validation (LOOCV) approach was also employed to ensure that the data for testing was from a participant that the classifier has never seen. Using LOOCV, we found the best average classification accuracy was reduced to 83.80% using the same set of neuromarkers obtained from the ROI group with functional hyper-connections. This performance surpassed the results reported using wavelet-based features by approximately 15%. Overall, our work suggests that (1) certain ROIs are particularly effective for MCI detection, especially when multi-resolution wavelet biomarkers are employed for such diagnosis; (2) there exists a significant performance difference in system evaluation between research-based experimental design and clinically accepted evaluation standards.

Behavioural biometrics is becoming more and more popular. It is hard to find a sensor that is embedded in a mobile/wearable device, which cannot be exploited to extract behavioural biometric data. In this study, the authors give the reader an overview of mobile device behavioural biometric data and how this data is used in experiments, especially examining papers that introduce new datasets. They will not examine performance accomplished by the algorithms used since a system's performance is enormously affected by the data used, its amount and quality. Altogether, 40 papers are examined, assessing how often they are cited, have databases published, what modality data are collected, and how the data is used. They offer a roadmap that should be taken into account when designing behavioural data collection and using collected data. They further look at the General Data Protection Regulation, and its significance to the scientific research in the field of biometrics. It is possible to conclude that there is a need for publicly available datasets with comprehensive experimental protocols, similarly established in facial recognition.

Background User interaction with a mobile device predominantly consists of touch motions, otherwise known as swipe gestures, which are used as a behavioural biometric modality to verify the identity of a user. Literature reveals promising verification accuracy rates for swipe gesture authentication. Most of the existing studies have considered constrained environment in their experimental set-up. However, real-life usage of a mobile device consists of several unconstrained scenarios as well. Thus, our work aims to evaluate the stability of swipe gesture authentication across various usage scenarios of a mobile device. Methods The evaluations were performed using state-of-the-art touch-based classification algorithms-support vector machine (SVM), k-nearest neighbour (kNN) and naive Bayes-to evaluate the robustness of swipe gestures across device usage scenarios. To simulate real-life behaviour, multiple usage scenarios covering stationary and dynamic modes are considered for the analysis. Additionally, we focused on analysing the stability of verification accuracy for time-separated swipes by performing intra-session (acquired on the same day) and inter-session (swipes acquired a week later) comparisons. Finally, we assessed the consistency of individual features for horizontal and vertical swipes using a statistical method. Results Performance evaluation results indicate impact of body movement and environment (indoor and outdoor) on the user verification accuracy. The results reveal that for a static user scenario, the average equal error rate is 1%, and it rises significantly for the scenarios involving any body movement-caused either by user or the environment. The performance evaluation for time-separated swipes showed better verification accuracy rate for swipes acquired on the same day compared to swipes separated by a week. Finally, assessment on feature consistency reveal a set of consistent features such as maximum slope, standard deviation and mean velocity of second half of stroke for both horizontal and vertical swipes. Conclusions The performance evaluation of swipe-based authentication shows variation in verification accuracy across different device usage scenarios. The obtained results challenge the adoption of swipe-based authentication on mobile devices. We have suggested ways to further achieve stability through specific template selection strategies. Additionally, our evaluation has established that at least 6 swipes are needed in enrolment to achieve acceptable accuracy. Also, our results conclude that features such as maximum slope and standard deviation are the most consistent features across scenarios.

This paper is concerned with the effect of ageing on biometric systems and particularly its impact on face recognition systems. Being biological tissue in nature, facial biometric trait undergoes significant changes as a person ages. Consequently, developing biometric applications for long-term use becomes a particularly challenging task. The idea behind the investigation presented here is that biometric systems have uneven difficulty in recognising people from different ages. Some algorithms may perform better for certain age groups. Therefore, a carefully optimised multi-algorithmic system can reduce the error rates. A subset of 100 subjects from the MORPH-II database has been selected to test the performance of a face verification system. The population is split into 5 age bands (≤19, 20-29, 30-39, 40-49, ≥50 years) based on their age during enrolment. The facial image database used in the experiments here contains images acquired over a period of five years. In the proposed multi-classifier scheme, features extracted from face images are transformed by different projection algorithms prior to matching. It has been observed that all the age groups showed improved performances when compared to the single classifier error rates. Of all the groups, the EER were highest for the younger population (≤ 19 year olds).