基于圆弧扫描线的手势特征提取和实时手势识别

基于手势的人机交互是当前备受关注的自然人机交互模式之一，实时手势识别是其中最重要的步骤。本文提出了一种基于圆弧扫描线的手势特征提取和实时手势识别方法。首先，基于一种抽象描述手掌和五指关系的简洁人手海龟模型，结合肤色特征和腕部标记分割出人手部图像，并进行二值化处理 和统一尺寸来建立手势训练集。 然后，以手掌中心为圆心构造同心圆来提取训练集中不同手势样本的特征，并使用线性判别分析(Linear discriminant analysis,LDA)算法对手势特征向量进行离线预处理。最后，使用 改进的加权K近邻（Weighted-K-nearest neighbor,W-KNN）算法进行实时手势分类和识别。为了验证本文方法的有效性 ，在自建小型手势数据库上进行了算法分析和比较，并在多投影系统下进行实时交互测试。实验 结果表明本文算法具有较高的识别效率。

Gesture Feature Extraction and Recognition Based on Circular Scan Lines

Human computer interaction based on hand gesture is one of the most popular natural interactive modes, which severely depends on the methods for real-time gesture recognition. Here, an effective hand feature extraction method is described, and the corresponding hand gesture recognition method is proposed. First, based on a simple tortoise model, one segments the human hand images by skin color features and tags on the wrist, and normalizes them to create the train set. Then feature vectors are computed by drawing concentric circles according to the center of the palm, and linear discriminant analysis (LDA) algorithm is used to deal with those vectors. Finally, an improved K-nearest neighbor (KNN) algorithm is presented to achieve real-time hand gesture classification and recognition. Experimental results with a self-defined hand gesture data set and multi-projector display systems prove the efficiency of the new approach.