The main goal of this thesis is to test the feasibility of human identification using the Electrocardiogram (ECG). Such biomedical signal has several key advantages including its intrinsic nature and liveness indicator which makes it more secure compared to some of the existing conventional and traditional biometric modalities. In compliance with the terms and regulations of McMaster University, this work has been assembled into a sandwich thesis format which consist of three journal papers. The main idea of this work is to identify individuals using distance measurement techniques and ECG feature matching. In addition, we gradually developed the content of the three papers. In the first paper, we started with the general criteria for developing ECG based biometric systems. To explain, we proposed both fiducial and non-fiducial approaches to extract the ECG features followed by providing comparative study on the performance of both approaches. Next, we applied non-overlapped data windows to extract the ECG morphological and spectral features. The former set of features include the amplitude and slope differences between the Q, R and S peaks. The later features include extracting magnitudes of the ECG frequency components using short time Fourier Transform (STFT). In addition, we proposed a methodology for QRS detection and segmentation using STFT and binary classification of ECG fiducial features. In the second paper, we proposed a technique for choosing overlapped data windows to extract the abovementioned features. Namely, the dynamic change in the ECG features from heart beats to heartbeat is utilized for identification purposes. To improve the performance of the proposed techniques we developed Frechet-mean based classifier for this application. These classifiers exploit correlation matrix structure that is not accounted for in classical Euclidean techniques. In addition to considering the center of the cluster, the Frechet-mean based techniques account for the shape of the cluster as well. In the third paper, the thesis is extended to address the variability of ECG features over multiple records. Specifically, we developed a multi-level wavelet-based filtering system which utilizes features for multiple ECGs for human identification purposes. In addition, we proposed a soft decision-making technique to combine information collected from multi-level identification channels to reach a common final class. Lastly, we evaluated the robustness of all our proposed methods over several random experiments by changing the testing data and we achieved excellent results. The results of this thesis show that the ECG is a promising biometric modality. We evaluated the performance of the proposed methods on the public ECG ID database because it was originally recorded for biometric purposes. In addition, to make performance evaluation more realistic we used two recordings of the same person obtained under possibly different conditions. Furthermore, we randomly changed both the training and testing data which are obtained from the full ECG records for performance evaluation purposes. However, it is worth mentioning that in all parts of the thesis, various parameters settings are presented to support the main ideas and it is subject to change according to human activity and application requirements. Finally, the thesis concludes with a comparison between all the proposed methods, and it provides suggestions on few open problems that can be considered for future research as extension to the work that has been done in this thesis. Generally, these problems are associated with the constraints on computational time, data volume and ECG clustering. / Thesis / Doctor of Philosophy (PhD)

The dissertation investigates the promises and challenges for application of cardiac signals in biometrics and affective computing, and noninvasive measurement of cardiac signals. We have mainly discussed two major cardiac signals: electrocardiogram (ECG), and photoplethysmogram (PPG). ECG and PPG signals hold strong potential for biometric authentications and identifications. We have shown that by mapping each cardiac beat from time domain to an angular domain using a limit cycle, intra-class variability can be significantly minimized. This is in contrary to conventional time domain analysis. Our experiments with both ECG and PPG signal shows that the proposed method eliminates the effect of instantaneous heart rate on the shape morphology and improves authentication accuracy. For noninvasive measurement of PPG beats, we have developed a systematic algorithm to extract pulse rate from face video in diverse situations using video magnification. We have extracted signals from skin patches and then used frequency domain correlation to filter out non-cardiac signals. We have developed a novel entropy based method to automatically select skin patches from face. We report beat-to-beat accuracy of remote PPG (rPPG) in comparison to conventional average heart rate. The beat-to-beat accuracy is required for applications related to heart rate variability (HRV) and affective computing. The algorithm has been tested on two datasets, one with static illumination condition and the other with unrestricted ambient illumination condition. Automatic skin detection is an intermediate step for rPPG. Existing methods always depend on color information to detect human skin. We have developed a novel standalone skin detection method to show that it is not necessary to have color cues for skin detection. We have used LBP lacunarity based micro-textures features and a region growing algorithm to find skin pixels in an image. Our experiment shows that the proposed method is applicable universally to any image including near infra-red images. This finding helps to extend the domain of many application including rPPG. To the best of our knowledge, this is first such method that is independent of color cues. / Ph. D.

Mobile devices users are storing more and more private and often highly sensitive information on their mobiles. Protective measures to ensure that users of mobile devices are appropriately safeguarded are thus imperative to protect users. Traditional mobile login methods, like numerical or graphical passwords, are vulnerable to passive attacks. It is common for criminal s to gain access to victims' personal information by watching victims enter their passwords into their cellphone screens from a short distance away. With this in mind, a Biometric authentication algorithm based on electrocardiogram or ECG is proposed. In this system the user will only need to touch the ECG electrodes of the mobile device to gain access. With this authentication mode no one will be able to see the biometric pattern that is used to unlock the de vices. This will increase the protection for the users. The algorithm was tested with ten subjects from MCRlab at the University of Ottawa at different days and conditions using a two electrode ECG phone case. Several tests were performed in order to reach the best setting for the algorithm to work properly. The final results show that the system has a 1.41% of chance to accept false users and 81.82% of accepting the right users. The algorithm was also tested with 73 subjects from Physionet database and the results were around the same, which confirms the consistency of the algorithm. This is the first approach on mobile authentication using ECG biometric signals and shows a promising future for this technology to be used in mobiles.