关键词: 手势识别, 尺度不变特征变换, 特征包, 视觉码书, BOF-SVM模型

Abstract: According to the influence of approximate skin color information or complex background, it is hard to get precise gesture contour by hand gesture segmentation, which will have effect on later gesture recognition rate and real-time interaction. Therefore, this paper proposed a gesture recognition method based on the BOF-SVM (Bag Of Features-Support Vector Machine). At first, local invariant features of the gesture images were extracted by the Scale Invariant Feature Transformation (SIFT) algorithm. Then the visual code book was generated by gesture local eigenvector (SIFT descriptors) through K-means clustering. And visual code set of every image got quantized by visual code book. As a result, the characterized vector of gesture images with fixed dimensional was obtained to train multi-class SVM classifier. This method only needed to frame the gesture area instead of segmenting gesture accurately. The experimental results indicate that the average recognition rate of the nine interactive hand gestures based on this method can reach 92.1%. Besides, it has good robustness and efficiency, and can adapt to the changes of environment.

Key words: Hand Gesture Recognition, SIFT(Scale Invariant Feature Transformation), Bag of Feature(BOF), Visual Code Book, BOF-SVM model