![]() Multimodal systems, unlike unimodal ones, take input from single or multiple sensors for measuring two or more different biometric characteristics. Most of the traditional biometric systems are unimodal systems, where a single biometric trait of the individual is used for identification. These biometrics are susceptible to errors due to users gaining experience, changing the way they act with a system. Behavioral templates cannot be obtained as easily as physiological ones given that authentication can take place dynamically throughout a user’s session as opposed to occur once statically so compromising a user should be more difficult. Behavioral biometrics can not only be used for static verification at the beginning of a user’s session, but can also be used dynamically to continuously authorize a user during a session. Studies in this field include keystroke dynamics, mouse dynamics, voice recognition, signature verification, Graphical User Interface (GUI) pattern analysis and touchscreen feature analysis. Behavioral biometrics are unique to a person in how they act or think. Physiological biometrics are usually used for a static verification that a user does once, and as such, leaves the computing session susceptible to attack afterwards. One downside of physiological biometrics is that if the biometric template is ever obtained and the user is compromised the system cannot be updated without body modification. They are useful because they do not change much for a user over time. Physiological methods include fingerprint scanning, iris scans, hand geometry recognition or facial recognition, all of which involve measuring physical characteristics of a person. There are two subsets of biometrics, physiological and behavioral. Keywordsīiometrics is the study of methods that uniquely identifies individuals based on their traits. After going through this process and investigating several Neural Network based classification models we conclude that combining the modalities results in a more accurate authentication decision and will become practical once the hardware is more widespread. In our system, mouse movement and eye movement data are gathered from each user simultaneously, a set of salient features are proposed, and a Neural Network classifier is trained on this data to uniquely identify users. In this paper, we seek to find out if combining both kinds of data produces better results. Previous biometric systems have attempted to identify users solely by eye or mouse data. 3 School of Engineering & Computing Sciences, New York Institute of Technology, Vancouver, Canada.2 Computer Science Department, Western Washington University, Bellingham, WA, USA.Jamison Rose 1, Yudong Liu 2 and Ahmed Awad 3 ![]() The list displayed below includes only those practitioners who have consented to be publicly listed on the Department’s public website.Īny MCP certifying practitioner can opt in (or out) to be publicly listed by changing the public list selection in the MCDMS Practitioner Profile section.Biometric Authentication Using Mouse and Eye Movement Data ![]() Please note that this list does not include all certifying practitioners. Public List of Consenting Medical Cannabis Program Practitioners
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