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.

Gait or walking pattern has been known as one of the alternative biometric solutions used in surveillance monitoring and control. The methods of gait recognition have been developed for decades using various techniques in different concepts. This study is a review paper collecting gait recognition approaches in both perspectives of model-based approaches relying on key joints/parts of the human body and appearance-based approaches relying on gait silhouettes. The existing methods addressing one of the most important real-world challenges, i.e. view changes, are emphasised and summarised in this study. Also, recent methods based on convolutional neural network solving the gait recognition and their challenges of view changes are illustrated. In addition, the publicly-available gait datasets and corresponding recognition performance and comparison are concluded in each section. The state-of-the-art gait recognition methods can achieve up to a perfect score of 100% accuracy for the normal walking, and above 80% in average for the view changes ranging from 0° to 180°.

The brain activity of a person sensed using electroencephalograph (EEG) has a unique neuronal signature consisting of relevant subject-specific information, which can be used for biometric identification. The main challenge in brainwave-based biometric identification is the extraction of robust features from non-stationary EEG signal that provide sufficient discriminability to differentiate between individuals. In this study, the authors propose an EEG-based biometric identification technique using a novel feature called frequency-weighted power (FWP), which offers higher discrimination in the person identification compared to the state of the art EEG features. FWP is an equivalent representation of the power of a specific frequency band, obtained by multiplying the specific frequency with its corresponding power density value and summing up it over the specific band. The efficacy of the proposed method is validated using resting-state EEG from online PhysioNet database as well as using resting-state EEG data acquired from 16 subjects in the laboratory during the experiment. Using a correlation-based classifier, the proposed method achieves an equal error rate (EER) of 0.0039 from eyes-closed resting-state EEG signals using 20 electrodes, which is nearly one-fifth of the EER obtained by the best method reported in the literature for the comparable number of electrodes.

Online signature verification (OSV) is a widely utilised technique in the medical, e-commerce and m-commerce applications to lawfully bind the user. These high-speed systems demand faster writer verification with a limited amount of information along with restrictions on training and storage cost. This study makes two major contributions: (i) A competent feature fusion technique in which traditional statistical-based features are fused with deep representations from a convolutional auto-encoder; and (ii) a hybrid architecture combining depth-wise separable convolution neural network (DWSCNN) and long short term memory (LSTM) network delivering state-of-the-art performance for OSV is proposed. DWSCNN is utilised for extracting deep feature representations and LSTM is competent in learning long term dependencies of stroke points of a signature. This hybrid combination accomplishes better classification accuracy (lower error rates) even with one-shot learning, i.e. achieving higher classification accuracies with only one training signature sample per user. The authors have extensively evaluated their model using three widely used datasets MCYT-100, SVC and SUSIG. These exhaustive experimental studies confirm that the DeepFuseOSV framework results in the state-of-the-art outcome by achieving an equal error rate (EER) of 13.26, 2.58, 0.07% in Skilled 1, Skilled 10 and Random 10 categories of MCYT-100, respectively, 7.71% in Skilled 1 category of SVC, 1.70% in Random 1 category of SUSIG.

The recent technological advances in surveillance, forensic and biometric systems to deter or even reduce the increasing number of crimes and prevent them is still questionable. The use of gait biometrics has attracted unprecedented interest due to its capability to work with low-resolution footage recorded from a distance. In contrast to mainstream research on gait biometrics which uses holistic silhouette features, the authors investigate the use of the bottom dynamic section within the human body to derive the most discriminative features for gait recognition. A new descriptor based on 7 Hu's moments is proposed describing the inner lower limb regions between the limbs being extracted only from landmark frames within one gait cycle. In order to assess the discriminatory potency of gait features from the lower regions for people identification, a number of experiments are conducted on the CASIA-B gait database to investigate the recognition rates using the KNN classifier and deep learning. The comparative analysis is performed against well-established research studies which were tested on the CASIA-B data set. The obtained results confirm the consistency of features extracted from the lower regions for gait recognition even under the impact of various factors.

The vulnerability of face recognition systems against so-called morphing attacks has been revealed in the past years. Recently, different kinds of morphing attack detection approaches have been proposed. However, the vast majority of published results has been obtained from rather constrained experimental setups. In particular, most investigations do not consider variations in morphing techniques, image sources, and image post-processing. Hence, reported performance rates can not be maintained in realistic scenarios, as the NIST FRVT MORPH performance evaluation showed. In this work, existing algorithms are benchmarked on a new, more realistic database. This database consists of two different data sets, from which morphs were created using four different morphing algorithms. In addition, the database contains four different post-processings (including print-scan transformation and JPEG2000 compression). Further, a new morphing attack detection method based on a fusion of different configurations of Multi-scale Block Local Binary Patterns (MB-LBP) on an image divided into multiple cells is presented. The proposed score-level fusion of a maximum number of 18 different configurations is shown to significantly improve the robustness of the resulting morphing attack detection scheme, yielding an average performance between 2.26% and 8.52% in terms of Detection Equal Error Rate (D-EER), depending on the applied post-processing.

Downsampling is critical for coding-based methods to reduce storage and accelerate matching speed. In coding-based palmprint recognition methods, the image size of the region of interest is typically 128 × 128, which is divided into 32 × 32 blocks and each block consists of 4 × 4 pixels. In the traditional downsampling method, the upper-left pixel in each block is selected to represent the feature of this block. However, this crude technique likely leads to serious information loss. The feature template heavily depends on the upper-left pixels, which degrades the tolerance for pixel-level dislocation and rotation. The authors analyse the downsampling stage in depth, and propose a democratic voting downsampling method (DVDM), which can improve the robustness and accuracy of the coding-based palmprint recognition methods without any prior knowledge. All the pixels in each block have equal voting rights to determine the feature of this block, so DVDM can extract stable features and effectively overcome the autocracy of an upper-left pixel. The sufficient experiments tested on the public PolyU palmprint dataset to confirm that DVDM can remarkably improve the robustness to pixel-level dislocation and rotation, and also improve accuracy performance, equal error rates of the coding-based methods are dropped down at most 11.5%.

Pervasive and useR fOcused biomeTrics bordEr projeCT (PROTECT) is an EU project funded by the Horizon 2020 research and Innovation Programme. The main aim of PROTECT was to build an advanced biometric-based person identification system that works robustly across a range of border crossing types and that has strong user-centric features. This work presents the case study of the multibiometric verification system developed within PROTECT. The system has been developed to be suitable for different borders such as air, sea, and land borders. The system covers two use cases: the walk-through scenario, in which the traveller is on foot; the drive-through scenario, in which the traveller is in a vehicle. Each deployment includes a different set of biometric traits and this study illustrates how to evaluate such multibiometric system in accordance with international standards and, in particular, how to overcome practical problems that may be encountered when dealing with multibiometric evaluation, such as different score distributions and missing scores.