FGFF Descriptor and Modified Hu Moment-Based Hand Gesture Recognition

Abstract

Gesture recognition has been studied for decades and still remains an open problem. One important reason is that the features representing those gestures are not sufficient, which may lead to poor performance and weak robustness. Therefore, this work aims at a comprehensive and discriminative feature for hand gesture recognition. Here, a distinctive Fingertip Gradient orientation with Finger Fourier (FGFF) descriptor and modified Hu moments are suggested on the platform of a Kinect sensor. Firstly, two algorithms are designed to extract the fingertip-emphasized features, including palm center, fingertips, and their gradient orientations, followed by the finger-emphasized Fourier descriptor to construct the FGFF descriptors. Then, the modified Hu moment invariants with much lower exponents are discussed to encode contour-emphasized structure in the hand region. Finally, a weighted AdaBoost classifier is built based on finger-earth mover’s distance and SVM models to realize the hand gesture recognition. Extensive experiments on a ten-gesture dataset were carried out and compared the proposed algorithm with three benchmark methods to validate its performance. Encouraging results were obtained considering recognition accuracy and efficiency.

Static hand gesture recognition based on hierarchical decision and classification of finger features

“…Some length parameters of the palms were used to divide the hand gestures into different types firstly, and the area-perimeter ratio and effective-area ratio of the hand gesture were extracted for gesture recognition. Zhang et al 36 proposed a distinctive fingertip gradient orientation with finger Fourier descriptor and modified Hu moments for depth gesture images collected by Kinect sensor. A weighted AdaBoost classifier based on finger-earth mover's distance and SVM models was used to realize the hand gesture recognition.…”