This thesis presents design and development of a gesture recognition system to recognize finger spelling American Sign Language hand gestures. We developed this solution using the latest deep learning technique called convolutional neural networks. This system uses blink detection to initiate the recognition process, Convex Hull-based hand segmentation with adaptive skin color filtering to segment hand region, and a convolutional neural network to perform gesture recognition. An ensemble of four convolutional neural networks are trained with a dataset of 25254 images for gesture recognition and a feedback unit called head pose estimation is implemented to validate the correctness of predicted gestures. This entire system was developed using Python programming language and other supporting libraries like OpenCV, Tensor flow and Dlib to perform various image processing and machine learning tasks. This entire application can be deployed as a web application using Flask to make it operating system independent.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc1404616 |
| Date | 12 1900 |
| Creators | Viswavarapu, Lokesh Kumar |
| Contributors | Namuduri, Kamesh, Li, Xinrong, Guturu, Parthasarathy |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | vii, 50 pages, Text |
| Rights | Public, Viswavarapu, Lokesh Kumar, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
Page generated in 0.0132 seconds