计算机科学 ›› 2019, Vol. 46 ›› Issue (6A): 165-168.
石雨鑫, 邓洪敏, 郭伟林
SHI Yu-xin, DENG Hong-min, GUO Wei-lin
摘要: 静态手势识别在人机交互方面具有重要的应用价值,但手势背景的复杂性和手势形态的多样性给识别的准确性带来了一定的影响。为了提高手势识别的准确率,文中提出了一种基于卷积神经网络(Convolution Nenral Network,CNN)与随机森林(Random Forest,RF)的识别方法。该方法首先对静态手势的图片进行手势分割,然后利用卷积网络的特征提取功能提取特征向量,最后使用随机森林分类器对这些特征向量进行分类。一方面,卷积神经网络具有分层学习的能力,能够收集图片上更具代表性的信息;另一方面,随机森林对样本和特征选择具有随机性,并且对每个决策树结果进行了平均,不易出现过拟合问题。在静态手势数据集上进行验证,实验结果显示:所提方法能有效地对静态手势进行识别,平均识别率能够达到94.56%。文中进一步将所提方法与几种经典的特征提取方法(主成分分析(PCA)和局部二进制(LBP))进行对比,实验结果显示:相比于PCA和LBP特征提取方法,由CNN提取的特征向量进行分类识别的效果更好,该方法的识别率比PCA-RF方法高2.44%,比LBP-RF方法高1.74%。最后,在经典的MNIST数据集上进行验证,所提方法的识别率达到了97.9%,高于其他两种传统的特征提取方法。
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