Hand gesture detection and recognition using principal component analysis

Nasser H. Dardas, E. Petriu
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引用次数: 67

Abstract

This paper presents a real time system, which includes detecting and tracking bare hand in cluttered background using skin detection and hand postures contours comparison algorithm after face subtraction, and recognizing hand gestures using Principle Components Analysis (PCA). In the training stage, a set of hand postures images with different scales, rotation and lighting conditions are trained. Then, the most eigenvectors of training images are determined, and the training weights are calculated by projecting each training image onto the most eigenvectors. In the testing stage, for every frame captured from a webcam, the hand gesture is detected using our algorithm, then the small image that contains the detected hand gesture is projected onto the most eigenvectors of training images to form its test weights. Finally, the minimum Euclidean distance is determined between the test weights and the training weights of each training image to recognize the hand gesture.
基于主成分分析的手势检测与识别
本文提出了一种基于皮肤检测和面部减除后的手势轮廓比较算法的实时裸手检测与跟踪系统,以及基于主成分分析(PCA)的手势识别系统。在训练阶段,训练一组不同尺度、旋转和光照条件下的手势图像。然后,确定训练图像的最大特征向量,并通过将每个训练图像投影到最大特征向量上计算训练权值。在测试阶段,对于从网络摄像头捕获的每一帧,使用我们的算法检测手势,然后将包含检测到的手势的小图像投影到训练图像的最特征向量上以形成其测试权值。最后,确定每个训练图像的测试权值与训练权值之间的最小欧氏距离,以识别手势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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