Scale-space feature detection is one of the most frequently used method in hand gesture recognition based on geometric model. However

the traditional method of scale-space feature detection involves heavy computation of Gaussian convolution

which makes the detection and recognition time-costly. In this paper

a fast scale-space feature detection method is proposed. First

a series of simple rectangular feature templates are used to approximate the complicated Gaussian derivatives convolution templates

with which the fast detectors of scale-space geometric features are obtained. After the detection of blob and ridge structures in gesture image

palm and finger structures are described and then gesture recognition is performed according to the configuration of palm and fingers. Then

integral image is used to rapidly calculate the convolution of rectangular feature templates

so the detection of scale-space geometric features is greatly accelerated in the method. Experiments on the standard dataset and the natural scene dataset show that the proposed method significantly reduces the time cost of gesture recognition while keeping comparable accuracy with traditional method.