基于最优DAGSVM的服务机器人交互手势识别

针对目前服务机器人手势交互方法在输入方式自然性和识别方法可靠性方面的不足，提出采用结合人脸和人手的姿态作为输入方式，实现了一个基于最优有向无环图支持向量机(DAGSVM)的手势识别系统。系统采用分步细化特征检测过程，即先粗检肤色，然后分别利用人眼Gabor特征和人手边缘小波矩特征检测脸和手部，可克服背景中的肤色干扰，并显著提高特征提取的可靠性；综合利用脸手区域不变矩和手的位置信息组成混合特征向量，采用优化拓扑排序策略组织多个两分类支持向量机(SVM)，构成最优DAGSVM多分类器，达到比普通DAGSVM更高的多分类准确率。实验验证了该方法的有效性和可靠性，并用于实现一种自然友好的人机交互方式。

Optimal DAGSVM Based Posture Recognition for Human-robot Interaction

A vision-based posture recognition system is proposed utilizing Optimal DAGSVM (Directed Acyclic Graph Support Vector Machine) classifier to achieve natural and reliable human-robot interactions. Coarse-to-fine feature detection scheme extracts skin-colored candidate regions, followed by face and hand verifications with Gabor filtered eye features and wavelet-moments of hand edge respectively. Statistical invariant moments and relative coordinates of face and hand regions are calculated as pattern feature vectors.A set of binary SVM classifiers are combined using Decision Directed Acyclic Graph with optimal structure to construct a more accurate multi-class DAGSVM classifier. Experimental result validates the reliable performance of the approach, where a natural and friendly interaction is achieved with a service robot.