A natural approach to convey numerical digits using hand activity recognition based on hand shape features

H. Chidananda; T. Hanumantha Reddy

doi:10.1117/12.2280239

19 June 2017 A natural approach to convey numerical digits using hand activity recognition based on hand shape features

H. Chidananda, T. Hanumantha Reddy

Proceedings Volume 10443, Second International Workshop on Pattern Recognition; 1044305 (2017) https://doi.org/10.1117/12.2280239
Event: Second International Workshop on Pattern Recognition, 2017, Singapore, Singapore

Abstract

This paper presents a natural representation of numerical digit(s) using hand activity analysis based on number of fingers out stretched for each numerical digit in sequence extracted from a video. The analysis is based on determining a set of six features from a hand image. The most important features used from each frame in a video are the first fingertip from top, palm-line, palm-center, valley points between the fingers exists above the palm-line. Using this work user can convey any number of numerical digits using right or left or both the hands naturally in a video. Each numerical digit ranges from 0 to9. Hands (right/left/both) used to convey digits can be recognized accurately using the valley points and with this recognition whether the user is a right / left handed person in practice can be analyzed. In this work, first the hand(s) and face parts are detected by using YCbCr color space and face part is removed by using ellipse based method. Then, the hand(s) are analyzed to recognize the activity that represents a series of numerical digits in a video. This work uses pixel continuity algorithm using 2D coordinate geometry system and does not use regular use of calculus, contours, convex hull and datasets.

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H. Chidananda and T. Hanumantha Reddy "A natural approach to convey numerical digits using hand activity recognition based on hand shape features", Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 1044305 (19 June 2017); https://doi.org/10.1117/12.2280239

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KEYWORDS

Video

Cameras

Binary data

Facial recognition systems

Chromium

Feature extraction

Calculus

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