Biometric authentication is a one-to-one verification process. A successful biometric system must be capable of avoiding a fraudulent claim while at the same time ensuring the privacy of individuals. This paper presents a novel framework for using the electrocardiogram (ECG) as a biometric system for human authentication. A new feature extraction technique known as pulse active ratio (PAR) is derived and used to generate novel ECG feature vectors. The proposed method is validated by experiments on 112 subjects, performing 9800 ECG comparisons providing a 10% improvement when compared to conventional temporal and amplitude feature extraction methods.

Deepfake videos and images have increased in recent years at exponential rates, and refer to the transfer of features of interest from source image (or video) to target image (or video), such that the target modality appears to animate the source almost close to the real. The forensic professionals, policy makers, and the public alike are interested in the role of Deepfakes is playing to confuse viewers in spreading misinformation by undermining the truth. In the past decade, computer vision has made significant advances using the latest state-of-art-methods of image processing techniques. Supervised Deep learning models produce super-human results in a variety of computer vision and machine learning applications. The field of medical imaging is scarce in terms of a reliable data set that is extensive enough to train distinct models. One way to tackle this problem is to use a Generative Adversarial Network to synthesise DEEPFAKE images to augment the data. DEEPFAKE images can be a useful means in various applications like translating to different useful and sometimes malicious modalities, unbalanced datasets or increasing the amount of datasets. In this paper the data scarcity has been addressed by using Progressive Growing Generative Adversarial Networks (PGGAN). However, PGGAN consists of convolution layer that suffers from the training-related issues. PGGAN requires a large number of convolution layers in order to obtain high-resolution image training, which makes training a difficult task. In this work, a subjective self-attention layer has been added with convolution layers for efficient feature learning and the use of spectral normalization for training stabilization - the two tasks resulting into what is referred to as Enhanced-GAN. The Enhanced-GAN performance is compared to PGGAN performance using the parameters ofAMScore and Mode Score. In addition, the strength of Enhanced-GAN and PGGAN synthesis is evaluated using the U-net model for segmentation tasks. Dice Coefficient metrics show that U-net trained on Enhanced-GAN DEEPFAKE data optimized with real data performs better than PGGAN DEEPFAKE data with real data.

Face Detection is an important step in any face recognition systems, for the purpose of localizing and extracting face region from the rest of the images. There are many techniques, which have been proposed from simple edge detection techniques to advance techniques such as utilizing pattern recognition approaches. This paper evaluates two methods of face detection, her features and Local Binary Pattern features based on detection hit rate and detection speed. The algorithms were tested on Microsoft Visual C++ 2010 Express with OpenCV library. The experimental results show that Local Binary Pattern features are most efficient and reliable for the implementation of a real-time face detection system.

Motion artifact noise in ECG processing is difficult to remove since its spectrum is known to overlap the ECG signal spectrum. The combination of wavelet based denoising and high-pass/low-pass filtering is presented and shown to provide good motion artifact noise removal capabilities. The relative performance of the new technique is demonstrated using ECGs from the MIT-BIH ECG database.

This paper presents a new feature extraction method known as Pulse Active Width (PAW) implemented on electrocardiogram (ECG) for biometric authentication. This method is developed based on the concept of Pulse Width Modulation (PWM). A total of 196 ECGs from 98 subjects, taken from the Physikalisch-Technische Bundesanstalt (PTB) database are used in the simulations. Biometric performance profile such as the area under ROC (AUR) and equal error rate (EER) are then used to evaluate the results. It is shown that the PAW method outperforms conventional temporal feature extraction techniques.