Abstract
Hand gestures with finger relationships are among the toughest features to extract for machine recognition. In this paper, this particular research challenge is addressed with 3D hand joint features extracted from distance measurements which are then colour mapped as spatio temporal features. Further patterns are learned using an 8-layer convolutional neural network (CNN) to estimate the hand gesture. The results showed a higher degree of recognition accuracy when compared to similar 3D hand gesture methods. The recognition accuracy for our dataset KL 3DHG with 220 classes was around 94.32%. Robustness of the proposed method was validated with only available benchmark 3D skeletal hand gesture dataset DGH 14/28.
Highlights
Hand gestures were considered to be one of the most powerful form of communication known to humans
In contrast to the above sensors for 3D hand capture, we propose a 3D motion capture technology-based hand gesture recognition
We present a detailed description of the methods used in 3D hand gesture recognition with deep convolutional neural network (CNN)
Summary
Hand gestures were considered to be one of the most powerful form of communication known to humans. It has evolved with the progression of generations which has been regarded as the formidable communication between humans and machines. There are only three sensors that are exclusively available for capturing 3D hand and fingers. They are Kinect [2], leap motion [3] and Time of Flight (ToF) [4] sensors. Leap motion is a good choice for hand capture but the factors for quality depends on the precision movements on the sensor, which at times attracts failures. Apart from the above, the most popular currently are based on 3D depth sensing technologies [5]
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