基于手势几何分布特征的手势识别
|
摘要
针对手势受缩放、旋转的影响导致识别率低的问题,提出了一种基于手势几何分布的特征提取方法用于手势识别。首先对分割后的手势图像进行归一化,并计算手势主方向和手势轮廓的最小外接矩形的宽长比,利用相似度函数进行初步识别,筛选出部分候选手势;再利用轮廓分割法统计手势轮廓点在极坐标内的分布情况,使用修正Hausdorff距离作为相似性度量的方法识别出最终手势。实验结果表明,所提方法能够快速且准确地识别各类手势,平均识别率达到92.89%,误识率降低到3.53%,识别速度较同类算法提高了4.2倍。
Aiming at the problem that gestures are affected by scaling and rotation,resulting in low recognition rate,this paper proposed a feature extraction method based on hand geometric distribution for gesture recognition.Firstly,the segmented gesture image is normalized.Secondly,width-to-length ratio of the minimum circumscribed rectangle of gesture main direction and gesture contour is calculated,and the similarity function is used as preliminary recognition to select some candidate gestures.Finally,contour segmentation method is used to estimate the distribution of gesture contour points in polar coordinates and the modified Hausdorff distance is used as a similarity measure method to identify the final gesture.The experimental results show that the proposed method can identify various gestures quickly and accurately,the average recognition rate reaches 92.89%,the false recognition rate is reduced to 3.53%,and the recognition speed is 4.2 times higher than that of similar algorithms.
Aiming at the problem that gestures are affected by scaling and rotation,resulting in low recognition rate,this paper proposed a feature extraction method based on hand geometric distribution for gesture recognition.Firstly,the segmented gesture image is normalized.Secondly,width-to-length ratio of the minimum circumscribed rectangle of gesture main direction and gesture contour is calculated,and the similarity function is used as preliminary recognition to select some candidate gestures.Finally,contour segmentation method is used to estimate the distribution of gesture contour points in polar coordinates and the modified Hausdorff distance is used as a similarity measure method to identify the final gesture.The experimental results show that the proposed method can identify various gestures quickly and accurately,the average recognition rate reaches 92.89%,the false recognition rate is reduced to 3.53%,and the recognition speed is 4.2 times higher than that of similar algorithms.
引文
[1] 易靖国,程江华,库锡树.视觉手势识别综述[J].计算机科学,2016,43(S1):103-108.
[2] 胡卫谊.基于动态手势的人机交互系统研究[D].武汉:武汉理工大学,2010.
[3] YANG C J,HAN D K,KO H.Continuous hand gesture recogni- tion based on trajectory shape information[J].Pattern Recognition Letters,2017,99(1):39-47.
[4] LI C,XIE C Y,ZHANG B C,et al.Deep Fisher discriminant learning for mobile hand gesture recognition[J].Pattern Recognition,2018,77:276-288.
[5] LI Y,WANG X G,LIU W Y,et al.Deep attention network for joint hand gesture localization and recognition using static RGB-D images[J].Information Sciences,2018,441:66-78.
[6] 余问鼎,刘春,邵雄凯.基于手指几何特征的移动设备视觉手势识别研究[J].黄冈师范学院学报,2015,35(6):74-79.
[7] 巫康伟,袁明新,葛玉婷,等.一种联合几何特征和隐马尔可夫模型的手势识别算法[J].自动化技术与用,2017,36(6):108-111.
[8] 郭桂旭,惠斌,冯剑.基于改进Hausdorff距离的分层景象匹配算法[J].计算机应用研究,2014,31(12):3837-3840.
[9] SHU X,WU X J.A novel contour descriptor for 2D shape matching and its application to image retrieval[J].Image and Vision Computing,2011,29(4):286-294.
[10] MEMO A,MINTO L,ZANUTTIGH P.Exploiting Silhouette Descriptors and Synthetic Data for Hand Gesture Recognition[M].The Eurographics Association,2015.
[11] MEMO A,ZANUTTIGH P.Head-mounted gesture controlled interface for human-computer interaction[J].Multimedia Tools and Applications,2018,77(1):27-53.
[12] 文芳,康彩琴,陈立文.等.基于RGBD数据的静态手势识别[J].计算机与现代化,2018(1):74-77.
[13] 王艳,徐诗艺,谌海云.基于特征距离加权的手势识别[J].计算机科学,2017,44(S1):220-223.
[14] 林水强,吴亚东,陈永辉.基于几何特征的手势识别方法[J].计算机工程与设计,2014,35(2):636-640.
[2] 胡卫谊.基于动态手势的人机交互系统研究[D].武汉:武汉理工大学,2010.
[3] YANG C J,HAN D K,KO H.Continuous hand gesture recogni- tion based on trajectory shape information[J].Pattern Recognition Letters,2017,99(1):39-47.
[4] LI C,XIE C Y,ZHANG B C,et al.Deep Fisher discriminant learning for mobile hand gesture recognition[J].Pattern Recognition,2018,77:276-288.
[5] LI Y,WANG X G,LIU W Y,et al.Deep attention network for joint hand gesture localization and recognition using static RGB-D images[J].Information Sciences,2018,441:66-78.
[6] 余问鼎,刘春,邵雄凯.基于手指几何特征的移动设备视觉手势识别研究[J].黄冈师范学院学报,2015,35(6):74-79.
[7] 巫康伟,袁明新,葛玉婷,等.一种联合几何特征和隐马尔可夫模型的手势识别算法[J].自动化技术与用,2017,36(6):108-111.
[8] 郭桂旭,惠斌,冯剑.基于改进Hausdorff距离的分层景象匹配算法[J].计算机应用研究,2014,31(12):3837-3840.
[9] SHU X,WU X J.A novel contour descriptor for 2D shape matching and its application to image retrieval[J].Image and Vision Computing,2011,29(4):286-294.
[10] MEMO A,MINTO L,ZANUTTIGH P.Exploiting Silhouette Descriptors and Synthetic Data for Hand Gesture Recognition[M].The Eurographics Association,2015.
[11] MEMO A,ZANUTTIGH P.Head-mounted gesture controlled interface for human-computer interaction[J].Multimedia Tools and Applications,2018,77(1):27-53.
[12] 文芳,康彩琴,陈立文.等.基于RGBD数据的静态手势识别[J].计算机与现代化,2018(1):74-77.
[13] 王艳,徐诗艺,谌海云.基于特征距离加权的手势识别[J].计算机科学,2017,44(S1):220-223.
[14] 林水强,吴亚东,陈永辉.基于几何特征的手势识别方法[J].计算机工程与设计,2014,35(2):636-640.