Name
Abstract | ||
|---|---|---|
This paper presents a novel method for computer vision-based static and dynamic hand gesture recognition. Haar-like feature-based cascaded classifier is used for hand area segmentation. Static hand gestures are recognized using linear discriminant analysis (LDA) and local binary pattern (LBP)-based feature extraction methods. Static hand gestures are classified using nearest neighbor (NN) algorithm. Dynamic hand gestures are recognized using the novel text-based principal directional features (PDFs), which are generated from the segmented image sequences. Longest common subsequence (LCS) algorithm is used to classify the dynamic gestures. For testing, the Chinese numeral gesture dataset containing static hand poses and directional gesture dataset containing complex dynamic gestures are prepared. The mean accuracy of LDA-based static hand gesture recognition on the Chinese numeral gesture dataset is 92.42%. The mean accuracy of LBP-based static hand gesture recognition on the Chinese numeral gesture dataset is 87.23%. The mean accuracy of the novel dynamic hand gesture recognition method using PDF on directional gesture dataset is 94%. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1142/S0219467814500065 | INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS |
Keywords | Field | DocType |
Hand gesture recognition, linear discriminant analysis, local binary pattern, principal directional features, Chinese numeral gesture, directional gesture, nearest neighbor | k-nearest neighbors algorithm,Computer vision,Pattern recognition,Gesture,Computer science,Local binary patterns,Gesture recognition,Feature extraction,Artificial intelligence,Linear discriminant analysis,Classifier (linguistics),Numeral system | Journal |
Volume | Issue | ISSN |
14 | 1-2 | 0219-4678 |
Citations | PageRank | References |
2 | 0.83 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mahmood Jasim | 1 | 4 | 2.37 |
| Tao Zhang | 2 | 422 | 100.57 |
| Md. Hasanuzzaman | 3 | 49 | 8.75 |