基于特征包支持向量机的手势识别

Hand gesture recognition based on bag of features and support vector machine

针对类肤色信息或复杂背景的影响,难以通过手势分割得到精确手势轮廓而对后期手势识别率与实时交互的影响,提出了一种基于特征包支持向量机(BOF-SVM)的手势识别方法。采用SIFT算法提取手势图像局部不变性特征点,将手势局部特征向量(尺度不变特征变换(SIFT)描述子)进行K-means聚类生成视觉码书,并通过视觉码书量化每一幅手势图像的视觉码字集合,以此获得手势图像的固定维数的表征向量来训练支持向量机(SVM)多类分类器。该方法只需框定手势所在区域,无需精确地分割人手。实验表明,该方法对9种交互手势的平均识别率达到92.1%,并具有很好的鲁棒性及实时性,能适应环境的变化。

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.