Dense SIFT–Flow based Architecture for Recognizing Hand Gestures
S.S. Suni, K. Gopakumar · Advances in Science Technology and Engineering Systems Journal · 2020
Several challenges like changes in brightness, dynamic background, occlusion and inconsistency of camera position make the recognition of hand gestures difficult in any vision-based method.Diversity in finger shape, size, distribution and motion dynamics is also a big constraint.This leads to the motivation in developing a dense Scale Invariant Feature Transform (SIFT) flow based architecture for recognizing dynamic hand gestures.Initially, a combination of three frames differencing and skin filtering technique is used for hand detection to reduce the computational complexity followed by a SIFT flow technique to extract the features from the detected hand region.SIFT flow vectors obtained from every pixel can lead to overfilling, data redundancy and dimension disaster.A dual layer belief propagation algorithm is utilized to optimize the feature vectors to resolve the dimensionality problem.Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers are used to evaluate the performance of the developed framework.Experiments were conducted on hand gesture database for HCI, Sebastien Marcel Dynamic Hand Posture Database and RWTH German finger spelling database.The simulation results demonstrate that the developed architecture has excellent performance on the uneven background and varying camera position and it is robust against image noise.A comparative analysis with the state of the art methods illustrates the effectiveness of the architecture.