Hand Gesture Recognition with Gaussian Scaling and Kirsch Edge Rotation

Hand gesture recognition is a vital aspect of robotic vision models. This paper presents a fusion based approach for hand gesture recognition. In this approach, we first extract the Gaussian scale space of an image and compute features on different scales. Kirsch's convolution mask is then applied on the feature map. The aim of the proposed approach is to remove unwanted information extract scale, rotation, and illumination invariant patterns from hand gestures. The final feature vector is aggregated through the concatenation of multiscale histograms. The Support Vector Machine classifier is demonstrated using extracted features. Moreover, we calculate the progress efficiency of proposed methods on three distinct databases by conducting experiments viz, Thomson, Bochum, and HGRI. The proposed method achieves classification accuracies of 94.25%, 92.77%, and 95.78% respectively on the investigated databases that outperform the existing approaches for hand gesture recognition.