The detection of spoofing by 3D mask in a 2D identity recognition system

Single heartbeat ECG authentication: a 1D-CNN framework for robust and efficient human identification

This study proposes a small one-dimensional convolutional neural network (1D-CNN) framework for individual authentication, considering the hypothesis that a single heartbeat as input is sufficient to create a robust system. A short segment between R to R of electrocardiogram (ECG) signals was chosen to generate single heartbeat samples by enforcing a rigid length thresholding procedure combined with an interpolation technique. Additionally, we explored the benefits of the synthetic minority oversampling technique (SMOTE) to tackle the imbalance in sample distribution among individuals. The proposed framework was evaluated individually and in a mixture of four public databases: MIT-BIH Normal Sinus Rhythm (NSRDB), MIT-BIH Arrhythmia (MIT-ARR), ECG-ID, and MIMIC-III which are available in the Physionet repository. The proposed framework demonstrated excellent performance, achieving a perfect score (100%) across all metrics (i.e., accuracy, precision, sensitivity, and F1-score) on individual NSRDB and MIT-ARR databases. Meanwhile, the performance remained high, reaching more than 99.6% on mixed datasets that contain larger populations and more diverse conditions. The impressive performance demonstrated in both small and large subject groups emphasizes the model's scalability and potential for widespread implementation, particularly in security contexts where timely authentication is crucial. For future research, we need to examine the incorporation of multimodal biometric systems and extend the applicability of the framework to real-time environments and larger populations.

Face presentation attack detection: Research opportunities and perspectives

The rapid development of biometric methods and their implementation in practice has led to the widespread attacks called spoofing, which are purely biometric vulnerabilities, but are not used in conjunction with other IT security solutions. Although biometric recognition as a branch of computer science dates back to the 1960s, attacks on biometric systems have become more sophisticated since the 2010s due to great advances in pattern recognition. It should be noted that face recognition is the most attractive topic for deceiving recognition systems. Popular presentation attacks, such as print, replay and mask attacks, have demonstrated a high security risk for SOTA face recognition systems. Many Presentation Attack Detection (PAD) methods (also known as face anti-spoofing methods or countermeasures) have been proposed that can automatically detect and mitigate such targeted attacks. The article presents a systematic survey in face anti-spoofing with prognostic trends in this research area. A brief description of 16 outstanding previous surveys on the face PAD field is mentioned, from which it is possible to trace how this scientific topic has developed. SOTA in PAD provides an analysis of a wide range of the PAD methods, which are categorized into two unbalanced groups: digital (feature-based) and physical (sensor-based) methods. Generalization of deep learning methods as a recent trend aimed at improving recognition results requires special attention. This survey presents five types of generalization such as transfer learning, anomaly detection, few-shot and zero-shot learning, auxiliary supervision, and multi-spectral methods. A summary of over than 40 existing 2D/3D face spoofing databases is a guideline for those who want to select databases for experiments. One can also find a description of performance evaluation metrics and testing protocols. In addition, we discuss trends and perspectives in the emerging field of facial biometrics.

A cascaded deep-learning-based model for face mask detection

Purpose

This work aims to present a deep learning model for face mask detection in surveillance environments such as automatic teller machines (ATMs), banks, etc. to identify persons wearing face masks. In surveillance environments, complete visibility of the face area is a guideline, and criminals and law offenders commit crimes by hiding their faces behind a face mask. The face mask detector model proposed in this work can be used as a tool and integrated with surveillance cameras in autonomous surveillance environments to identify and catch law offenders and criminals.

Design/methodology/approach

The proposed face mask detector is developed by integrating the residual network (ResNet)34 feature extractor on top of three You Only Look Once (YOLO) detection layers along with the usage of the spatial pyramid pooling (SPP) layer to extract a rich and dense feature map. Furthermore, at the training time, data augmentation operations such as Mosaic and MixUp have been applied to the feature extraction network so that it can get trained with images of varying complexities. The proposed detector is trained and tested over a custom face mask detection dataset consisting of 52,635 images. For validation, comparisons have been provided with the performance of YOLO v1, v2, tiny YOLO v1, v2, v3 and v4 and other benchmark work present in the literature by evaluating performance metrics such as precision, recall, F1 score, mean average precision (mAP) for the overall dataset and average precision (AP) for each class of the dataset.

Findings

The proposed face mask detector achieved 4.75–9.75 per cent higher detection accuracy in terms of mAP, 5–31 per cent higher AP for detection of faces with masks and, specifically, 2–30 per cent higher AP for detection of face masks on the face region as compared to the tested baseline variants of YOLO. Furthermore, the usage of the ResNet34 feature extractor and SPP layer in the proposed detection model reduced the training time and the detection time. The proposed face mask detection model can perform detection over an image in 0.45 s, which is 0.2–0.15 s lesser than that for other tested YOLO variants, thus making the proposed detection model perform detections at a higher speed.

Research limitations/implications

The proposed face mask detector model can be utilized as a tool to detect persons with face masks who are a potential threat to the automatic surveillance environments such as ATMs, banks, airport security checks, etc. The other research implication of the proposed work is that it can be trained and tested for other object detection problems such as cancer detection in images, fish species detection, vehicle detection, etc.

Practical implications

The proposed face mask detector can be integrated with automatic surveillance systems and used as a tool to detect persons with face masks who are potential threats to ATMs, banks, etc. and in the present times of COVID-19 to detect if the people are following a COVID-appropriate behavior of wearing a face mask or not in the public areas.

Originality/value

The novelty of this work lies in the usage of the ResNet34 feature extractor with YOLO detection layers, which makes the proposed model a compact and powerful convolutional neural-network-based face mask detector model. Furthermore, the SPP layer has been applied to the ResNet34 feature extractor to make it able to extract a rich and dense feature map. The other novelty of the present work is the implementation of Mosaic and MixUp data augmentation in the training network that provided the feature extractor with 3× images of varying complexities and orientations and further aided in achieving higher detection accuracy. The proposed model is novel in terms of extracting rich features, performing augmentation at the training time and achieving high detection accuracy while maintaining the detection speed.

A hybrid tiny YOLO v4-SPP module based improved face mask detection vision system

Law offenders take advantage of face masks to conceal their identities and in the present time of the COVID-19 pandemic wearing face masks is a new norm which makes it a daunting task for the investigation agencies to identify the offenders. To address the issue of detection of people wearing face masks using surveillance cameras, we propose a novel face mask vision system that is based on an improved tiny YOLO v4 object detector. The face masks detection network of the proposed vision system is developed by integrating tiny YOLO v4 with spatial pyramid pooling (SPP) module and additional YOLO detection layer and tested and validated on a self-created face masks detection dataset consisting of more than 50,000 images. The proposed tiny YOLO v4-SPP network achieved a mAP (mean average precision) value of 64.31% on the employed dataset which was 6.6% higher than tiny YOLO v4. Specifically, for detection of the presence of a small object like a face mask on the face region, the proposed tiny YOLO v4-SPP based vision system achieved an AP (average precision) of 84.42% which was 14.05% higher than the original tiny YOLO v4 thus, ensuring that the proposed network is capable of accurate detection of a mask on the face region in real-time surveillance applications where visibility of complete face area is a guideline.

Improving the Security and QoE in Mobile Devices through an Intelligent and Adaptive Continuous Authentication System

Continuous authentication systems for mobile devices focus on identifying users according to their behaviour patterns when they interact with mobile devices. Among the benefits provided by these systems, we highlight the enhancement of the system security, having permanently authenticated the users; and the improvement of the users’ quality of experience, minimising the use of authentication credentials. Despite the benefits of these systems, they also have open challenges such as the authentication accuracy and the adaptability to new users’ behaviours. Continuous authentication systems should manage these challenges without forgetting critical aspects of mobile devices such as battery consumption, computational limitations and response time. With the goal of improving these previous challenges, the main contribution of this paper is the design and implementation of an intelligent and adaptive continuous authentication system for mobile devices. The proposed system enables the real-time users’ authentication by considering statistical information from applications, sensors and Machine Learning techniques based on anomaly detection. Several experiments demonstrated the accuracy, adaptability, and resources consumption of our solution. Finally, its utility is validated through the design and implementation of an online bank application as proof of concept, which allows users to perform different actions according to their authentication level.