计算机科学 ›› 2020, Vol. 47 ›› Issue (7): 103-110.doi: 10.11896/jsjkx.200100073
刘肖1, 袁冠1,2, 张艳梅1, 闫秋艳1, 王志晓1
LIU Xiao1, YUAN Guan1,2, ZHANG Yan-mei1, YAN Qiu-yan1, WANG Zhi-xiao1
摘要: 为了提高基于可穿戴设备手势识别的性能,针对单分类器在手势识别时会出现偏向性的问题,提出了基于自适应多分类器融合的手势识别方法(Self-adaptive Multi-classifiers Fusion,SAMCF)。首先,针对统计特征无法表征复杂手势之间类内变异性和相似性的问题,SAMCF使用卷积神经网络(Convolutional Neural Network,CNN)自动提取具有强表征能力的深度特征;然后,采用多个基本分类器对提取的特征向量进行识别,并通过自适应融合算法决策出最优识别结果,解决了单分类器的偏向性问题;最后,基于数据手套采集的数据集,验证了模型的鲁棒性和泛化能力。实验结果表明,SAMCF能够有效地提取手势的深度特征,解决单分类器的偏向性问题,提高了手势识别的效率,增强了手势识别的性能,对字符级手势(美国手语和阿拉伯数字)识别的准确率达到98.23%,较其他算法平均提高了5%;对单词级手势(中国手语)识别的准确率达到97.81%,较其他算法平均提高了4%。
中图分类号:
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