Morse Code Based Secured Authentication System through Machine Learning
Author(s):
Mamatha B N, Priyanka R, Varsha S, Shubhankar R
Keywords:
Machine learning, Authentication System, Eye Blink, Morse code, Personal Identification Numbers, Face Recognition.
Abstract
Data science is a multidisciplinary blend of data inference, algorithm development and technology in order to solve analytically complex problems. Data science is used by almost all the industries like instructive organizations, finance, medical services, business to deal with huge volumes of information. The pragmatic applications range from foreseeing stock development to anticipating disease; utilized in picture preparing to character acknowledgment, sound handling for discourse to message expectation. Since the majority of individuals on the planet are dealing with issues in the field of verification and security. The system provides a real time eye tracing for password authentication for people who authenticate themselves using Morse code. Advancement in the technology of authentication and authorization has be supported in the 21st century a lot as we know. Personal identification numbers (PIN) are widely used for user authentication and security since the late 90's. Since PIN numbers are easily crack able these days, people prefer to follow different approach. PIN validation with hands-off look-based PIN section procedures, then again, abandons no actual impressions and in this manner offers a safer secret word passage alternative. Gaze-based system for authentication alludes to discovering the eye area across consecutive picture frames and following the eye movements by plotting the eye center. Password authentication will be done using Morse code, where numbers will be represented in dots and dashes. This model presents a real-time application for gaze-based PIN entry with face recognition, and eye detection and tracking for PIN identification using a smart camera.
Article Details
Unique Paper ID: 152162

Publication Volume & Issue: Volume 8, Issue 2

Page(s): 569 - 574
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