A human computer interactions framework for biometric user identification

Computer assisted functionalities and services have saturated our world becoming such an integral part of our daily activities that we hardly notice them. In this study we are focusing on enhancements in Human-Computer Interaction (HCI) that can be achieved by natural user recognition embedded in the employed interaction models. Natural identification among humans is mostly based on biometric characteristics representing what-we-are (face, body outlook, voice, etc.) and how-we-behave (gait, gestures, posture, etc.) Following this observation, we investigate different approaches and methods for adapting existing biometric identification methods and technologies to the needs of evolving natural human computer interfaces

Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication

We investigate whether a classifier can continuously authenticate users based on the way they interact with the touchscreen of a smart phone. We propose a set of 30 behavioral touch features that can be extracted from raw touchscreen logs and demonstrate that different users populate distinct subspaces of this feature space. In a systematic experiment designed to test how this behavioral pattern exhibits consistency over time, we collected touch data from users interacting with a smart phone using basic navigation maneuvers, i.e., up-down and left-right scrolling. We propose a classification framework that learns the touch behavior of a user during an enrollment phase and is able to accept or reject the current user by monitoring interaction with the touch screen. The classifier achieves a median equal error rate of 0% for intra-session authentication, 2%-3% for inter-session authentication and below 4% when the authentication test was carried out one week after the enrollment phase. While our experimental findings disqualify this method as a standalone authentication mechanism for long-term authentication, it could be implemented as a means to extend screen-lock time or as a part of a multi-modal biometric authentication system.Comment: to appear at IEEE Transactions on Information Forensics & Security; Download data from http://www.mariofrank.net/touchalytics

Biometric Validation by Storing different Patterns using Mouse Gesture Signatures

In this paper, the construct Authentication of automatic data processing system by Mouse Gestures was summarized and its significance towards its Methodologies was illustrated. The Authentication of ancient ways that like victimization text parole or image parole results in less secure and harder to user to recollect. Based on Neural Network formula and its analysis has been user to attain the Biometric Authentication based on user behavior on Neural Network and is additionally surveyed. This paper conjointly conducts a review of the realm of Artificial Neural Network and biometric methods that add another layer of security to computing system. DOI: 10.17762/ijritcc2321-8169.160413

Android Based Behavioral Biometric Authentication via Multi-Modal Fusion

Because mobile devices are easily lost or stolen, continuous authentication is extremely desirable for them. Behavioral biometrics provides non-intrusive continuous authentication that has much less impact on usability than active authentication. However single-modality behavioral biometrics has proven less accurate than standard active authentication. This thesis presents a behavioral biometric system that uses multi-modal fusion with user data from touch, keyboard, and orientation sensors. Testing of ve users shows that fusion of modalities provides more accurate authentication than each individual modalities by itself. Using the BayesNet classification algorithm, fusion achieves False Acceptance Rate (FAR) and False Rejection Rate (FRR) values of 9.65% and 2% respectively, each of which is 8% lower than the closest individual modality

Implementation of Mouse Gesture Recognition

In this paper, we construct Authentication of automatic data processing system by Mouse Gestures was summarized and its significance towards its Methodologies was illustrated. Based on Neural Network formula and its analysis has been user to attain the Biometric Authentication based on user behavior on Neural Network and is additionally surveyed. Our This research paper conducts a review of the realm of Artificial Neural Network and biometric methods that add another more secure layer of security to computing system. DOI: 10.17762/ijritcc2321-8169.150519