Biometrics and Bioinformatics

A. A. Randive ¹, H. B. Mali ², S. D. Lokhande ³

Affiliations
1 University of Pune, IN
2 Department of Electronics and Telecommunication Engineering, The University of Pune, IN
3 Department of Electronics from University of Kolhapur, IN

Abstract

The automatic recognition of gestures enriches Human – Computer Interaction by offering a natural intuitive method of data input. Compared with the traditional interaction approaches, such as keyboard, mouse, pen etc. Vision based hand interaction is more natural and efficient. As this methodology is aided with cameras and computer vision techniques. And besides normal use for handling objects and manipulating tools, the hand of human being can be used as the mean of communication here. This paper presents a new method or technique for a real time static hand gesture recognition on plain uniform background, for the HCI, based on pattern recognition using K-nearest Neighbor (KNN). Major consideration here is the stable lighting condition. Here work is done to recognize 10 gestures from ASL. Concept of connected components or objects is used here for hand segmentation, canny edge detector is used for pattern creation and classification is done by using K-nearest Neighbor algorithm. This algorithm gives 84% accuracy with recognition time of 0.3s. Accuracy decreases as distance of hand from camera increases above 1.5feets. The gestures recognized here can be used further to develop an application of controlling the devices which will be very much helpful to the physically disabled and older persons who can’t move easily from one place to another.

Keywords

American Sign Language (ASL), Gesture Recognition, Human Computer Interaction (HCI), K-Nearest Neighbor (KNN).

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Ajita A. Patil ¹, S. D. Lokhande ², H. B. Mali ³

Affiliations
1 Department of Electronics and Telecommunications, University of Pune, IN
2 Department of Electronics from University of Kolhapur, IN
3 Department of Electronics and Telecommunication Engineering, The University of Pune, IN

Abstract

In today’s fast lifestyle Safety of a human being is a major issue. The need to develop system model is because of the security issues of human being and improvement in automobile industry. The research report shows that Drowsiness leads to severe loss of health and wealth. So to reduce these all effects few techniques are created. This includes some standard approach and it will help to send warning signal to operator if he/she falls asleep. In this project the aim is to check drowsiness at real time with new hybrid model concept. The warning signals are developed at the end of detection methodology. The detection process includes capturing of video of operator, pattern recognition in Labview. After that analysis of parametric values comparison of total results are made. Finally according to the value of parameters awareness signal is given to the operator.

Keywords

Biological Signal, Drowsiness Level, Hybrid Approach, Sleepiness Scale.

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