Over the last decade, non-invasive physiological monitoring has become a high interest in research topics worldwide. Currently, we are living in a post-pandemic era, where any personalized system should promote contactless monitoring to reduce viruses and bacteria spread. According to the federal-based regulatory agency named U.S. Food and Drug Administration, digital personalized medicine and mobile health (mHealth) technologies have symbolized an efficient improvement in the healthcare system through many markets positions validations [1]. Biometric Authentication is a research topic that has benefited from the emergence of these research lines. Researchers’ main goal consists of improving cybersecurity and controlling access for users. Biometrically, a user could be authenticated in different ways by using different biological aspects which represent uniqueness across the systems. Physiological and behavioral aspects amplify the authentication scenarios’ scope, making these processes a reasonable challenge for researchers. It could be done by using non-biometrics methodologies, but it does not necessarily mean that uniqueness is given, making the non-biometrics solutions highly vulnerable to finding uniqueness across the users. Personalized medicine and mHealth technologies are good examples of how biometrics could be obtained from subjects non-invasively. Other than cybersecurity and controls access applications, many other applications have obtained the advantage of it, like early diagnosis, diseases detections, user wellness, etc. Many business models have brought innovation to the healthcare engineering market. Recently, smartphone devices with embedded sensors have provided the option for software developers to gather biometrical data from different aspects. Photoplethysmography is a non-invasive optical sensing technology that is capable of detecting volumetric changes in the circulatory system. Many biological features could be obtained by using this technology on the subjects (heart rate, blood pressure, etc.). Photoplethysmography signals represent a good promise for authentication context since unique characteristics can be found while gathering it from the individual’s physiology. Following the photoplethysmography sensing fundamentals, external artifacts are susceptible to the data acquisition procedures. But algorithms for those artifacts' removal can be developed to prevent wrong predictions. In summary, this dissertation investigates the advantages and limitations of the application of physiological signals in biometrics and non-invasively measurement of photoplethysmography signals using smartphones to improve cybersecurity and controls access while authenticating different subjects.