Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future

Machine Understanding of Human Behavior

A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior

ZEBRA: Zero-Effort Bilateral Recurring Authentication

Common authentication methods based on passwords, tokens, or fingerprints perform one-time authentication and rely on users to log out from the computer terminal when they leave. Users often do not log out, however, which is a security risk. The most common solution, inactivity timeouts, inevitably fail security (too long a timeout) or usability (too short a timeout) goals. One solution is to authenticate users continuously while they are using the terminal and automatically log them out when they leave. Several solutions are based on user proximity, but these are not sufficient: they only confirm whether the user is nearby but not whether the user is actually using the terminal. Proposed solutions based on behavioral biometric authentication (e.g., keystroke dynamics) may not be reliable, as a recent study suggests. \par To address this problem we propose ZEBRA. In ZEBRA, a user wears a bracelet (with a built-in accelerometer, gyroscope, and radio) on her dominant wrist. When the user interacts with a computer terminal, the bracelet records the wrist movement, processes it, and sends it to the terminal. The terminal compares the wrist movement with the inputs it receives from the user (via keyboard and mouse), and confirms the continued presence of the user only if they correlate. Because the bracelet is on the same hand that provides inputs to the terminal, the accelerometer and gyroscope data and input events received by the terminal should correlate because their source is the same – the user\u27s hand movement. In our experiments ZEBRA performed continuous authentication with 85% accuracy in verifying the correct user and identified all adversaries within 11 s. For a different threshold that trades security for usability, ZEBRA correctly verified 90% of users and identified all adversaries within 50 s

Dynamic Template Adjustment in Continuous Keystroke Dynamics

Dynamika úhozů kláves je jednou z behaviorálních biometrických charakteristik, kterou je možné použít pro průběžnou autentizaci uživatelů. Vzhledem k tomu, že styl psaní na klávesnici se v čase mění, je potřeba rovněž upravovat biometrickou šablonu. Tímto problémem se dosud, alespoň pokud je autorovi známo, žádná studie nezabývala. Tato diplomová práce se pokouší tuto mezeru zaplnit. S pomocí dat o časování úhozů od 22 dobrovolníků bylo otestováno několik technik klasifikace, zda je možné je upravit na online klasifikátory, zdokonalující se bez učitele. Výrazné zlepšení v rozpoznání útočníka bylo zaznamenáno u jednotřídového statistického klasifikátoru založeného na normované Euklidovské vzdálenosti, v průměru o 23,7 % proti původní verzi bez adaptace, zlepšení však bylo pozorováno u všech testovacích sad. Změna míry rozpoznání správného uživatele se oproti tomu různila, avšak stále zůstávala na přijatelných hodnotách.Keystroke dynamics is one of behavioural biometric characteristics which can be employed for continuous user authentication. As typing style on a keyboard changes in time, the template adapting is necessary. No study covered this topic yet, as far as the author knows. This master thesis tries to fill this gap. Several classification techniques were exercised with help of keystroke data from 22 volunteers in order to test if they can be improved to unsupervised online classifiers. A significant improvement in impostor recognition was noted at one-class statistical classifier based on normed Euclidean distance. The impostor could make 23.7 % actions less than in offline version on average but the improvement was obseved with all test sets. In contrary, the genuine user recognition varied from user to user but it still kept at acceptable values.

Affective games:a multimodal classification system

Affective gaming is a relatively new field of research that exploits human emotions to influence gameplay for an enhanced player experience. Changes in player’s psychology reflect on their behaviour and physiology, hence recognition of such variation is a core element in affective games. Complementary sources of affect offer more reliable recognition, especially in contexts where one modality is partial or unavailable. As a multimodal recognition system, affect-aware games are subject to the practical difficulties met by traditional trained classifiers. In addition, inherited game-related challenges in terms of data collection and performance arise while attempting to sustain an acceptable level of immersion. Most existing scenarios employ sensors that offer limited freedom of movement resulting in less realistic experiences. Recent advances now offer technology that allows players to communicate more freely and naturally with the game, and furthermore, control it without the use of input devices. However, the affective game industry is still in its infancy and definitely needs to catch up with the current life-like level of adaptation provided by graphics and animation