基于手语表达内容与表达特征的手语识别技术综述

陶唐飞; 刘天宇

doi:10.11999/JEIT221051

基于手语表达内容与表达特征的手语识别技术综述

doi: 10.11999/JEIT221051

陶唐飞^{1, 2, ,},
刘天宇²

1.
现代设计及转子轴承系统教育部重点实验室西安 710049
2.
西安交通大学机械工程学院西安 710049

基金项目: 陕西省重点研发计划(2020KWZ-003)

详细信息

作者简介:
陶唐飞：男，副教授，研究方向为面向智造、智能诊断的图像处理与机器视觉技术等

刘天宇：男，硕士生，研究方向为计算机视觉

通讯作者:
陶唐飞　taotangfei@mail.xjtu.edu.cn

中图分类号: TP3-05
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- 被引次数: 0
出版历程
- 收稿日期: 2022-08-10
- 修回日期: 2022-10-27
- 网络出版日期: 2022-11-07
- 刊出日期: 2023-10-31

A Survey of Sign Language Recognition Technology Based on Sign Language Expression Content and Expression Characteristics

TAO Tangfei^{1, 2
, ,},
LIU Tianyu²

1.
Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing System, Xi’an 710049, China
2.
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Funds: The Key Research and Development Program in Shaanxi Province of China (2020KWZ-003)

摘要

摘要: 手语识别(SLR)技术是打破听障人群与健听人群间交流壁垒的重要技术手段。该文综述了近几年的手语数据集、评价指标以及手语识别方法。首先，系统梳理了手语数据集并分析了手语识别方法的数据集发展方向。其次，详细介绍了手语识别方法的评价指标。然后，根据手语表达内容、手语识别方法所采用的特征分类总结分析了孤立词手语识别方法与连续语句识别方法、仅依靠手部特征的手语识别方法与多特征融合的手语识别方法。最后探讨了手语识别技术面临的挑战及其发展方向。
- 手语识别技术 /
- 手语数据集 /
- 孤立词手语识别 /
- 连续手语识别 /
- 多特征融合手语识别
Abstract: Sign Language Recognition (SLR) technology is an important technical means to break the communication barrier between hearing-impaired people and healthy people. The sign language datasets, evaluation indicators and sign language recognition methods in recent years are summarized. Firstly, the sign language dataset is systematically summarized and the development trend of the dataset of sign language recognition methods is analyzed. Secondly, the evaluation indicator of sign language recognition method is introduced in detail. Then, according to the content of sign language expression and the features used in sign language recognition methods, isolated word sign language recognition methods and continuous sign language recognition methods, sign language recognition methods relying only on hand features and sign language recognition methods of multi feature fusion are summarized and analyzed. Finally, the challenges and development direction of sign language recognition technology are discussed.

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图 1 手语零样本学习示意图

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图 2 多特征融合示意图

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图 3 本文所收录的手语识别模型在几种典型数据集下的识别表现

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表 1 孤立词手语数据集

建立年份	数据集名称	语言	样本数量	标签数量	数据形式	样本类型	录制人数	开放程度	真实场景
2007	GSL-20^[26]	希腊语	840	20	RGB	词语	6	请求	×
2007	GSL isol.^[27]	希腊语	40785	310	RGB-D	词语	7	注册	×
2011	ASLLVD^[28]	英语	9800	3300	RGB	词语	6	开放	×
2012	DGS Kinect 40^[29]	德语	3000	40	RGB-D/骨架	词语	15	请求	×
2013	PSL TOF 84^[30]	波兰语	1680	84	RGB-D	词语	1	开放	×
2014	PSL Kinect 30^[30]		300	30
	DEVISIGN-G^[23]	中文	432	36	RGB	词语	8	请求	×
	DEVISIGN-D^[23]		6000	200
	DEVISIGN-L^[23]		24000	500
2014	ChaLearn^[31]	英语	50000	249	RGB-D	词语	7	部分开放	×
2015	CSL-500^[9]	中文	125000	500	RGB-D/骨架	词语	50	开放	×
2016	LSA64^[32]	西班牙语	3200	64	RGB	词语	10	开放	×
2019	GSL^[33]	希腊语	40785	310	RGB-D	词语	7	请求	×
2019	WLASL2000^[34]	英语	21083	2000	RGB	词语	119	开放	多背景
2020	RKS-PERSIANSIGN^[35]	波斯语	10000	100	RGB	词语	10	开放	√(10)
2020	KSL^[36]	韩语	1229	77	RGB/光流	词语	20	开放	√
2021	NCSL^[24]	中文	90000	300	RGB	词语	30	请求	×
2021	NMFs-CSL^[25]	中文	32010	1067	RGB	词语	10	请求	×
2022	ASL-SKELETON3D^[21] ASL-Phono^[21]	英语	9747	3300	3D RGB	词语	6	请求	×
2022	ASLLRP Sign Bank^[37]	英语	41830	6000	RGB	词语		开放	×

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表 2 连续语句手语数据集

建立年份	数据集名称	语言	样本数量	标签数量	数据形式	样本类型	录制人数	开放程度	真实场景
2007	SIGNUM^[38]	德语	33 210	780	RGB	句子	20	开放	×
2007	GSL SD^[27]	希腊语	10 290	310	RGB	句子	7	请求	×
2007	GSL SI^[27]		10 290	310		句子	7		×
2012	RWTH-PHOENIX- Weather^[39]	德语	45 760	1 200	RGB	句子	9	开放	×
2015	CSL-100^[9]	中文	25 000	100	RGB-D/骨架	句子	50	开放	×
2016	LSE-Sign^[40]	西班牙语	2 400	2 400	RGB	句子	2	注册	√
2016	MSR^[22]	德语	33 210	450	RGB	句子	25		×
2018	RWTH-PHOENIX- Weather 2014T^[41]	德语	67 781	1 066	RGB	句子	9	开放	√
2019	GSL^[33]	希腊语	10 295	310	RGB-D	句子	7	请求	×
2019	How2Sign^[42]	英语	36 773	16 000	RGB-D/骨架/语音等	句子	11	开放	多场景

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表 3 孤立词手语识别方法

模型分类	方法	模型方法	数据集	Acc(%)	备注(工作关注点)
传统模型	图像处理	Canny边缘检测^[48]	ASL Alphabet ASL	99.00 84.30
	图像处理	Bag Of Features^[49]	自制英文字母数据集	85.20	阈值、颜色检测等
	特征提取	Bag Of Features^[49]	自制英文字母数据集	85.20	SURF、K-近邻等
		HOG-PCA^[50]	阿拉伯字母数据集	99.20	RGB
		SURF、SIFT^[51]	Kinect Depth Datasets	>80.00	比较两种变换效果
	分类识别	Quadratic SVM^[49]	自制英文字母数据集	85.20	比较2次与3次SVM
		SVM^[50]	阿拉伯字母数据集	99.20
		DTW-HMM^[52]	AUSLdataset	87.40,92.40
		CTC^[53]	Real-time	42.00	室外进行
神经网络	卷积神经网络	CNN, diffGrad优化^[54]	自制印度数据集	99.64	结合数据增强
		C3D, 2DCNN^[55]	Graffiti数据集 In-house	92.60 89.70	OFMT
		C3D^[56]	Kinect Datasets	94.20	多模态信息
		I3D^[57]	WLASL 2000 MS-ASL100	87.47 96.66	多特征、多模态
		R(2+1)D^[58]	CSL-500	97.45	预训练，注意力
	循环神经网络	LSTM^[59]	CSL-500	63.30
		LSTM-RNN结合k-近邻^[60]	ASL Fingerspelling	99.44
		BiLSTM^[61]	ASL Datasets	97.98	结合迁移学习
		Bi-ConvLSTM^[62]	ASL Datasets	98.81	实时摄像头下ACC为90%，结合迁移学习
		FFV-Bi-LSTM^[63]	ASL Datasets	98.33	体感系统
	图神经网络	STGC-Transformer^[64]	自制日本数据集	12.14(WER)	CTC结合交叉熵
	图神经网络	MS-G3D AUTSL^[65] MS-G3D LSE^[65]	WLASL2000	95.24 93.91	迁移学习
	GAN网络	H-GANs^[66]	ASLLVD RWTH-PHOENIX Weather 2014	1.40(CER) 20.70(WER)	20个特征融合
注意力机制	Spatial Temporal	3D CNN^[67]	CSL-500 ChaLearn14	88.70 95.30(Jaccard Index)	3DCNN提取时空特征结合时间,空间注意力
	Hierarchical Temporal	HTAN^[68]	CSL-500	93.10	分层时间注意力网络
	Global local	Res-C3D^[69]	CSL-500 DEVSIGN_D	89.20 91.00	全局-视频时间序列局部-目标检测定位
	Transformer	Transformer^[70]	WLASL-100 WLASL-300 LSA64	63.20 (TOP1) 43.80 (TOP1) 100.00	致力于小计算量模型
	BERT	BERT,3DCNN,LSTM^[71]	RKS-PERSIANSIGN ASLLVD	74.60 68.80	提取特征，权衡多模态，特征映射
迁移学习	特征	I3D^[72]	ChaLearn249	62.09	迁移时空特征
	共享参数	Alexnet,R-CNN^[73]	Turkish Sign Language	99.70
	共享参数	TensorFlow Object Detection API^[74]	自制印度手语数据集	85.45
零样本学习	Zero-Shot	3DCNN, LSTM^[75]	ASL-Text	51.40
零样本学习	Zero-Shot	LSTM, BERT^[71] C3D, VSD	RKS-PERSIANSIGN First-Person ASLVID isoGD	74.60 67.20 68.80 60.20	Multi-modal

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表 4 连续语句手语识别方法

时间(年)	模型方法	数据集	WER(%)	备注(工作关注点)
2002	HMM^[89]	97 German signs	91.70(ACC)	结合束搜索
2012	GHMM^[90]	SIGNUM	13.00	MLP
2017	CNN, LSTM^[91]	视频教材	98.43	双流2DCNN
2019	CNN-Transformer-CTC^[92]	PHOENIX-2014 PHOENIX-2014-T	26.00 26.10	特征提取, 上下文信息
2020	CNN-Transformer-CTC^[93]	PHOENIX-2014-T	24.59	手语识别+口语翻译
2021	CNN-BiLSTM-CTC^[94]	Indian Sign Language	15.14	孤立词迁移句子
2021	RNN-Transducer^[83]	CSL-100	6.10	H2SNet
2021	HST-GNN^[95]	PHOENIX-2014-T CSL-100	19.50 27.60	graph convolution graph self-attentions
2021	SLRGAN^[96]	PHOENIX-2014 CSL-100	23.40 2.10	语境信息
2021	CNN-Transformer-CTC^[97]	PHOENIX-2014	29.78	多模态，注意力
2022	CNN-Transformer-CTC^[98]	PHOENIX-2014-T PHOENIX-2014	22.90 23.20	相对位置编码
2022	GoogleLeNet-Tconvs-CTC^[91] 3D-ResNet-BLSTM-CTC^[91] I3D-BLSTM-CTC^[91]	PHOENIX-2014, CSL-100	46.41, 2.41 50.98, 13.36 52.71, 2.72	卷积网络对比研究

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表 5 基于模型所利用表达特征的手语识别方法

特征部位	年份	方法模型	数据集	Acc(%)	备注(工作关注点)
手部	2018^[106]	RBM	NYU ASL Fingerspelling A	90.01 98.13	multi-modal
	2018^[44]	CNN	STB Dexter EgoDexter	96.5(AUC) 64(AUC) 54(AUC)	Real-time Pose estimation Hand tracking
	2019^[104]	CNN	RWTH-BOSTON-50 ASLLVD	89.33 31.50	Hand tracking Pre-trained
	2019^[108]	CNN	Kinect and LM data	97.66	multi-modal
	2020^[109]	ASLNN	DGSLR dataset	96.78	Hand pose tracking
	2021^[48]	CNN	ASL Alphabet	99.00	Image processing
	2021^[105]	HMM, Camshift	ASSLRP dataset	77.75	Hand tracking
	2021^[107]	S2VT	USTC-SLR	95.60(98.40)	减少训练参数
	2021^[71]	LSTM, BERT C3D, VSD	RKS-PERSIANSIGN First-Person ASLVID isoGD	74.60 67.20 68.80 60.20	Zero-shot Transformer Multi-modal
	2021^[100]	MPH, SVM, GBM	Massey ASL Alphabet Finger Spelling A	99.39 87.60 98.45	Hand Pose Estimation (网络摄像头)
	2022^[102]	CNN, SVD	ASLVID	93.00	手部关键点
	2022^[101]	MPH	Thai Finger Spelling schemes	84.57(S1,S2) 23.66(P1)	hand-keypoint detection
	2022^[73]	Alexnet(预训练) R-CNN	Turkish Sign Language	99.70(AP)	Transfer learning
	2022^[110]	mRMR-PSO	ASL ISL dataset NUS Dataset II Arabic Dataset	84.30 98.70 92.06 85.60	多模型组合复杂背景环境
	2022^[74]	TensorFlow Object Detection API	Indian Sign Language	85.45	Real-time Transfer learning
手部、口型、表情、身体姿态等	2015^[48]	CNN, HMM	RWTH-PHOENIX-Weather	55.70(精度)	手部、口型
	2020^[111]	3DCNN	Bosphorus-Sign22k Turkish Isolated SL dataset	99.78	手、面部、身体按照权重融合训练
	2020^[15]	SMPL reverse	SURREAL Human3.6M datasets	62.3,40.8(mm) 提升25(mm)	姿态恢复(RGB-3D) 身体姿态
	2021^[112]	SMPL-X	GSLL Dataset	94.77	手、面部、身体结合光流+RGB，姿态恢复
	2021^[66]	H-GANs	RWTH-PHOENIX-Weather 2014 ASLLVD	20.70(WER) 1.40(CER)	手、脸型、头、眼睛等20种特征，参数优化，连续手语，降维
	2020^[25]	GLE-Net	NMFs-CSL SLR500	90.50 96.80	上下文关系，判别 fine-grained cues

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