Title :
A biometric security based electronic gadget control using hand gestures
Author :
Garg, Rajat ; Shriram, N. ; Gupta, Vikrant ; Agrawal, Vineet
Author_Institution :
Dept. of Electron. & Commun. Eng., VIT- Univ., Vellore, India
Abstract :
The control over electronic system using hand gestures is an innovative user interface that resolves the complications of using numerous remote controls for appliances. Founded on a unified set of hand gestures, this system interprets the user´s hand gestures into pre-specified commands to control one or many devices simultaneously. However, of late, security has been of major concern among the people in using such a system, especially, at critical places like Home Entrances, Cashbox, etc. In order to minimize this issue, we have incorporated Biometric security through Iris recognition that will help in user authentication. Iris recognition, which is a relatively new biometric technology, has great advantages such as variability, stability and security. This proves to be very propitious for high security environments. This paper presents an elaboration of the methodologies employed including object recognition, artificial neural networks, and biometric security systems. All image processing was performed using NI Vision Assistant. In addition, NI LabVIEW was used to train and implement Neural Networks for Hand Gesture Classification. We have developed an automatic hand-gesture based control system that works after authentication (using Iris recognition) of the user.
Keywords :
biometrics (access control); gesture recognition; home automation; learning (artificial intelligence); object recognition; NI vision assistant; artificial neural networks; biometric security systems; cashbox security; electronic gadget control; hand gesture classification; home entrance security; innovative user interface; iris recognition; neural network training; object recognition; user authentication; Artificial neural networks; Authentication; Biometrics; Control systems; Home appliances; Iris recognition; Object recognition; Security; Stability; User interfaces; Artificial neural network (ANN); biometric security; feature vectors; gesture classification; iris recognition pattern matching;
Conference_Titel :
Ultra Modern Telecommunications & Workshops, 2009. ICUMT '09. International Conference on
Conference_Location :
St. Petersburg
Print_ISBN :
978-1-4244-3942-3
Electronic_ISBN :
978-1-4244-3941-6
DOI :
10.1109/ICUMT.2009.5345495