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. / The heart is an integral part of the human body. With every beat, the heart continuously pumps oxygen-enriched blood to providing fuel to our cells and thus enabling life. The heartbeat is initiated by electrical signals generated in the heart muscles. This electrical activity, which are often governed by our autonomic nervous system, can be measured directly by electrocardiogram (ECG) using advanced and often obtrusive instrumentation. Photoplethysmogram (PPG), on the other hand, measures how the blood volume changes and can be readily measured with inexpensive instrumentation at certain locations (e.g. at the fingertip). The ECG and PPG are widely used cardiac signals in medical science for diagnosis and health monitoring. But, these signals hold greater potential than just its medical diagnostic applications. In this work, we have mainly investigated if these signals can be used to identify an individual. Every human heart differs by their size, shape, locations inside body, and internal structure. This motivated us to represent the signals using a mathematical model and use machine learning algorithm to identify individual persons. We have discussed how our method improves the identification accuracy and can be used with current biometric methods like fingerprint in our phone. The measurement procedures of cardiac signals are often cumbersome and need instruments which may not be available outside medical facilities. Therefore, we have investigated alternative method of remote photoplethysmography (rPPG) that are relatively inexpensive and unobtrusive. In this dissertation, we have used face video of an individual to extract the heart rate information. The flow of blood causes small changes in the color of face skin. This is not visible to human eyes without digital magnification, but we have shown how knowledge of distinct behavior of human heart rate and use of advanced computer vision algorithms helped us to extract vital signals like heart rate with a significant accuracy. In addition, to measure rPPG using face video, we integrated a method for automatic detection of skin from images and videos. Existing skin detection methods depended on color information which is not always available within available video sources. We have developed a novel standalone skin detection method to show that it is not necessary to have color cues for skin detection. Our method relies on the context and the texture based appearance of skin. To the best of our knowledge, this is first such method that is independent of color cues. In summary, the dissertation investigates the promises and challenges for application of cardiac signals in biometrics and nonobtrusive measurement of cardiac signals using face video.