基于线性判别分析和自适应K近邻法的手势识别
Hand Gesture Recognition with Linear Discriminant Analysis and Adaptive K-Nearest Neighbor Algorithms

基于小容量数据集的手势识别是人机交互技术研究中的一个重要课题。本文提出了一种基于线性判别分析和自适应K近邻法的手势识别方法。首先,应用高斯背景建模方法从包含目 标交互者的训练视频集中提取各类手型图像,并调整到相同尺度来构建手势训练集。然后,通过改进的线性判别分析对训练数据进行特征提取。最后提出一种自适应K近邻法对实时交互过程中得到的手型信息进行分类和识别。应用上述方法自建小型手势库进行实验和比较分析,结果显示与现有的手势识别算法相比，本文方法具有更高的识别率。
Gesture recognition based on small samples is one of the main trends in the advanced human-computer interaction research. A novel gesture recognition method based on adaptive K-nearest neighbor (A-KNN) and linear discriminant analysis (LDA) is presented. First, hand-shape images are segmented from the given interaction videos, and scaled to the same size to construct the training set. Then an optimized LDA algorithm is designed to extract gesture features. Finally, an improved KNN algorithm is introduced with adaptive Kvalue to classify the real-time gesture information. Test results show that the correct recognition rate of the proposed approach is higher than most existing methods. 