Abstract
This paper presents a feature extraction method for hand gesture based on multi-layer perceptron. The feature of hand skin color in the YCbCr color space is used to detect hand gesture. The hand silhouette and features can be accurately extracted in means of binarizing the hand image and enhancing the contrast. Median and smoothing filters are integrated to remove the noise. Combinational parameters of Hu invariant moment, hand gesture region, and Fourier descriptor are created to form a new feature vector which can recognize hand gesture. To confirm the robustness of this proposed method, a dataset including 3500 images is built. Experimental results demonstrate that our system can successfully recognize hand gesture with 97.4% recognition rate.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.