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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.

Steganography is the process used hide the existence of information during transmission. Cover mediums like text, image, audio, and video protocols are used to hide the secret information. This process helps to provide secret communication between two parties. As data is unknown, it is challenging to attract the attention of any third parties. Therefore, steganography becomes the best and most secure method for data transmission. Digital images are the most common cover media or carriers in steganographic processes, where the secret payload is embedded into images. Several techniques are coming under image steganography, and there includes a different method to ensure the secrecy of messages. This chapter gives an overview of the different commonly used techniques in this area and the latest existing image steganography methods and the comparison of techniques.

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.

Quaternions are widely used in physics, geometry and image processing, which are hyper-complex numbers applied in three-dimensional space. In this paper, we utilize the benefits of quaternions to present a new PRNG (pseudo-random number generator) algorithm using quaternions and three chaotic systems. The generated sequences yield to a long period and remedy the periodicity problem for cryptographic purposes. The performance of the proposed PRNG method is estimated through several NIST suit statistical analyses. The results demonstrate that the proposed PRNG algorithm has significant qualities for viable applications in security purposes.

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.

Epilepsy seizure prediction has become one of the interesting fields that attract researchers to innovate solutions. For epilepsy patients, Electroencephalography (EEG) signals consist of three activities: normal, pre-ictal and ictal. In order to design a prediction model for the ictal state, it is required to distinguish between the activities of EEG signals. This paper presents efficient seizure prediction approaches from EEG signals based on statistical analysis, digital band-limiting filters and artificial intelligence. Band-limiting filters are used to remove out-of-band noise and spurious effects. Then, statistical analysis is adopted for channel selection and seizure prediction based on a thresholding strategy. This statistical analysis depends on amplitude, median, mean, variance and derivative of the EEG signal. The adopted band-limiting filter affects the seizure prediction metrics such as accuracy, prediction time and false alarm rate. The prediction process consists of two phases: training and testing. Both k-means clustering and Multi-Layer Perceptron (MLP) networks are considered for seizure prediction based on artificial intelligence. The proposed approaches can be implemented in a mobile application to give alerts to patients or care givers. The simulation results reveal that the proposed approaches present high performance in terms of accuracy, prediction time and false alarm rate.