Name
Abstract | ||
|---|---|---|
In this paper, a new method of hand gesture recognition is proposed. First, the hand region is separated based on the depth information. Then the wavelet feature is calculated by enforcing the wavelet invariant moments of the hand region, and the distance feature is extracted by calculating the distance from fingers to hand centroid. Next, a feature vector which is composed of wavelet invariant moments and distance feature is generated. Finally, a support vector machine classifier based on the feature vectors is used to identify these hand gestures. Experimental results show that our method can achieve high accuracy, and can distinguish similar gestures well. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ISM.2017.91 | 2017 IEEE International Symposium on Multimedia (ISM) |
Keywords | Field | DocType |
component,hand gesture recognition,wavelet invariant moments,similar gestures,SVM | Computer vision,Feature vector,Pattern recognition,Computer science,Support vector machine,Gesture recognition,Feature extraction,Artificial intelligence,Invariant (mathematics),Centroid,Wavelet,Wavelet transform | Conference |
ISBN | Citations | PageRank |
978-1-5386-2938-3 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xi Liu | 1 | 122 | 20.80 |
| Chen Li | 2 | 80 | 54.64 |
| Lihua Tian | 3 | 22 | 9.29 |