Dynamic hand gesture recognition using vision-based approach for human–computer interaction

In this work, a vision-based approach is used to build a dynamic hand gesture recognition system. Various challenges such as complicated background, change in illumination and occlusion make the detection and tracking of hand difficult in any vision-based approaches. To overcome such challenges, a hand detection technique is developed by combining three-frame differencing and skin filtering. The three-frame differencing is performed for both colored and grayscale frames. The hand is then tracked using modified Kanade–Lucas–Tomasi feature tracker where the features were selected using the compact criteria. Velocity and orientation information were added to remove the redundant feature points. Finally, color cue information is used to locate the final hand region in the tracked region. During the feature extraction, 44 features were selected from the existing literatures. Using all the features could lead to overfitting, information redundancy and dimension disaster. Thus, a system with optimal features was selected using analysis of variance combined with incremental feature selection. These selected features were then fed as an input to the ANN, SVM and kNN model. These individual classifiers were combined to produce classifier fusion model. Fivefold cross-validation has been used to evaluate the performance of the proposed model. Based on the experimental results, it may be concluded that classifier fusion provides satisfactory results (92.23 %) compared to other individual classifiers. One-way analysis of variance test, Friedman’s test and Kruskal–Wallis test have also been conducted to validate the statistical significance of the results.