Hand gesture recognition using Gaussian threshold and different SVM kernels

Abstract

Hands play an important part in expressing one’s actions and ideas thus Hand Gesture Recognition (HGR) is very significant in computer vision based gesture recognition for Human Computer Interaction (HCI). In our work, the dataset has been generated for five hand gestures (Close Hand, Open Hand, Victory Hand, Thumb Down and Thumb Up), by making videos of 10 different users doing the gestures with all possible variations resulting in total 16,240 entries. Firstly we have used image processing algorithms like Bilateral Filter, Median Blur and Gaussian Threshold for smoothing the images and then compared the performance of different Support Vector Machine (SVM) kernels i.e. rbfdot, vanilladot, polydot, tanhdot, laplacedot and besseldot, for HGR. The accuracy achieved with different SVM kernels varied from 24.17% to 85.07% with training-testing ratio of 70–30% for 16,240 entries in the dataset. The 10-fold cross validation is performed to prove the robustness of the kernel with SVM.