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Today, biometrics are the preferred technologies for person identification, authentication, and verification cutting across different applications and industries. Sadly, this ubiquity has invigorated criminal efforts aimed at violating the integrity of these modalities. Our study presents a multi-biometric cancellable scheme (MBCS) that exploits the proven utility of deep learning models to fuse multi-exposure fingerprint, finger vein, and iris biometrics by using an Inspection V3 pre-trained model to generate an aggregate tamper-proof cancellable template. To validate our MBCS, we employed an extensive evaluation including visual, quantitative, and qualitative assessments as well as complexity analysis where average outcomes of 99.158%, 24.523 dB, 0.079, 0.909, 59.582 and 23.627 were recorded for NPCR, PSNR, SSIM, UIQ, SD and UACI respectively. These quantitative outcomes indicate that the proposed scheme compares favourably against state-of-the-art methods reported in the literature. To further improve the utility of the proposed MBCS, we are exploring its refinement to facilitate generation of cancellable templates for real-time biometric applications in person authentication at airports, banks, etc.

PurposeThe objective of this paper is to perform infrared (IR) face recognition efficiently with convolutional neural networks (CNNs). The proposed model in this paper has several advantages such as the automatic feature extraction using convolutional and pooling layers and the ability to distinguish between faces without visual details.Design/methodology/approachA model which comprises five convolutional layers in addition to five max-pooling layers is introduced for the recognition of IR faces.FindingsThe experimental results and analysis reveal high recognition rates of IR faces with the proposed model.Originality/valueA designed CNN model is presented for IR face recognition. Both the feature extraction and classification tasks are incorporated into this model. The problems of low contrast and absence of details in IR images are overcome with the proposed model. The recognition accuracy reaches 100% in experiments on the Terravic Facial IR Database (TFIRDB).

Recently, the interest in using wearable devices or the internet of things (IoT)-based biometric authentication, especially IoT-based electrocardiogram (ECG) has increased. ECG-based biometric authentication has received great attention as a next-generation promising technique and been implemented with various approaches to improve the authentication performance for the past few decades. However, ECG signals of a person may vary according to his/her physical states, or health conditions, possibly leading to authentication failure in some cases. Therefore, it is essential to design a robust method that handles the ECG subject variability for accurate authentication. In this Chapter, we proposed an efficient and robust authentication system based on ECG. In this study, we propose a novel deep learning fusion framework using the transfer learning concept where the deep features extracted from different models are combined into a single feature which are then fed to a custom classifier such as a support vector machine (SVM) for authentication. Cross-validation studies are used to assess the performance of the proposed authentication system using two public databases. Evaluation results show that the performance of our fusion model achieved an authentication accuracy of 99.4% with a high level of precision and recall. Finally, the results show that the proposed system is suitable for real-time applications.

This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes are traced to various reasons, including late detection of latent or asymptomatic carriers, migration, and inadequate isolation of infected people. This makes detection, containment, and mitigation global priorities to contain exposure via quarantine, lockdowns, work/stay at home, and social distancing that are focused on &ldquo, flattening the curve&rdquo, While medical and healthcare givers are at the frontline in the battle against COVID-19, it is a crusade for all of humanity. Meanwhile, machine and deep learning models have been revolutionary across numerous domains and applications whose potency have been exploited to birth numerous state-of-the-art technologies utilised in disease detection, diagnoses, and treatment. Despite these potentials, machine and, particularly, deep learning models are data sensitive, because their effectiveness depends on availability and reliability of data. The unavailability of such data hinders efforts of engineers and computer scientists to fully contribute to the ongoing assault against COVID-19. Faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the Convolutional Neural Network (CNN) and the Convolutional Long Short-Term Memory (ConvLSTM)-based deep learning models (DADLMs) and, by doing so, boost the accuracy of COVID-19 detection. Experimental results reveal improvement in terms of accuracy of detection, logarithmic loss, and testing time relative to DLMs devoid of such data augmentation. Furthermore, average increases of 4% to 11% in COVID-19 detection accuracy are reported in favour of the proposed data-augmented deep learning models relative to the machine learning techniques. Therefore, the proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus.

Such a hot open issue in the area of computer vision is the classification of visual images especially in Internet of Things (IoT) and remote mid-band and high-band based connections. In this paper, we propose a robust and efficient taxonomy framework. The proposed model utilizes the well-known convolutional neural network composites to construct a robust Visual Image Classification Network (VICNet). The VICNet consists of three convolutional layers, four Relu/Leaky Relu activation layers, three max-pooling layers and only two fully connected layers for extracting expected input image features. To make the training process faster, we used non-saturating neurons with a very efficient Graphics Processing Unit (GPU) implementation for the convolution operation. To minimize over-fitting issue in the fully-connected layers, we utilized a recently-developed regularization approach “dropout” with a dropping probability of 50%. The proposed VICNet framework has a high potential capability in the recognition of test images. The experimental and simulations results proven the efficacy of the proposed model.

Both automatic speaker identification (ASI) and speech recognition can be utlized now for the control of modern robots. An ASI algorithm can be implemented at a speech interface of the robot to determine the identity of the person allowed to deal with the robot, while speech recognition can be implemented for the interpretation of the order given to the robot. Robustness of the ASI system is a challenging task in the presence of speech degradations such as noise and interference. This study presents a new approach to improve the accuracy of speaker identification in the presence of interference for robot control applications with a convolutional neural network (CNN). First, the speech signal from the speaker is divided into segments, each of which is transformed into a spectrogram, and hence Radon transformation is estimated for this spectrogram. The spectrogram resolves the speech segment into a map of power distribution with both time and frequency. Together, the spectrograms and their Radon transforms are used as inputs to a proposed CNN-based deep learning model. Necessary refinements are undertaken and the resulting optimized “Radon-Deep-Learning Model (RDLM) is compared with a benchmark model. The proposed model consists of six convolutional (CNV) layers followed by six Max. pooling layers, while the benchmark model consists of three CNV layers followed by three Max. pooling layers. Experimental results reveal that the proposed RDLM model achieves a high classification accuracy up to 97.5%, which is more than double the performance reported for some traditional methods that are used for speaker identification.