Abstract
Dynamic hand gesture recognition is a subject which has been investigated almost from the beginning of using terminals to interact with the computer central unit. We present a method for dynamic hand gesture recognition with image source acquired by a single IR camera. First of all, the hand images are captured by one infrared camera, which are light independent, and not be limited by skin color. Second, due to the dynamic hand gesture sets contain more than one frame, the data dimension is very large and the adjacent frames have limited difference. We use the twofold selections to choose the key frames from the gesture image sets. The Gabor feature is used to describe the locality variation. And the Sparse representation based classification (SRC) is used for recognition. Experimental results on dynamic hand gesture images with variations of rotation and translation demonstrate the good performance of our method.
Highlights
Human computer interaction is one of the most visible and challenging research topics in computer vision and machine learning [1], [2]
Hand gestures as the most common used communication mode and hand recognition technology have become the focus of study in the field of computer vision and pattern recognition
It known that magnitude information contains the variation and local energy in the image.In [8], the Gabor feature vector χ is defined as via uniform down-sampling, normalization and concatenation of the Gabor filtering coefficients: Infrared camera
Summary
Human computer interaction is one of the most visible and challenging research topics in computer vision and machine learning [1], [2]. In this paper we use infrared imaging, which makes the hand segmentation more robust than based on skin color information method segment from the background. Different methods are proposed such as texture information, skin color, contour information [2] and shape parameters [3].Based on infrared images, exploring a robust feature extraction method is very important. The success of manifold learning implies that the high dimensional hand images can be sparsely represented or coded by the representative samples on the manifold. The SR based classification (SRC) is defined as evaluating which class of training samples could result in the minimal reconstruction error of the input testing image with the sparse coding coefficients. We use Gabor feature based sparse representation classification (GSRC) [9] to realize dynamic hand gesture recognition. The σ determines the ratio of the Gaussian window width to wavelength
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