A Survey on Artificial Intelligence in Chinese Sign Language Recognition

Chinese Sign Language (CSL) offers the main means of communication for the hearing impaired in China. Sign Language Recognition (SLR) can shorten the distance between the hearing-impaired and healthy people and help them integrate into the society. Therefore, SLR has become the focus of sign language application research. Over the years, the continuous development of new technologies provides a source and motivation for SLR. This paper aims to cover the most recent approaches in Chinese Sign Language Recognition (CSLR). With a thorough review of superior methods from 2000 to 2019 in CSLR researches, various techniques and algorithms such as scale-invariant feature transform, histogram of oriented gradients, wavelet entropy, Hu moment invariant, Fourier descriptor, gray-level co-occurrence matrix, dynamic time warping, principal component analysis, autoencoder, hidden Markov model (HMM), support vector machine (SVM), random forest, skin color modeling method, k-NN, artificial neural network, convolutional neural network (CNN), and transfer learning are discussed in detail, which are based on several major stages, that is, data acquisition, preprocessing, feature extraction, and classification. CSLR was summarized from some aspect as follows: methods of classification and feature extraction, accuracy/performance evaluation, and sample size/datasets. The advantages and limitations of different CSLR approaches were compared. It was found that data acquisition is mainly through Kinect and camera, and the feature extraction focuses on hand’s shape and spatiotemporal factors, but ignoring facial expressions. HMM and SVM are used most in the classification. CNN is becoming more and more popular, and a deep neural network-based recognition approach will be the future trend. However, due to the complexity of the contemporary Chinese language, CSLR generally has a lower accuracy than other SLR. It is necessary to establish an appropriate dataset to conduct comparable experiments. The issue of decreasing accuracy as the dataset increases needs to resolve. Overall, our study is hoped to give a comprehensive presentation for those people who are interested in CSLR and SLR and to further contribute to the future research.

[1]  Houqiang Li,et al.  Sign Language Recognition using 3D convolutional neural networks , 2015, 2015 IEEE International Conference on Multimedia and Expo (ICME).

[2]  Forrest N. Iandola,et al.  SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.

[3]  Yudong Zhang,et al.  Classification of Fruits Using Computer Vision and a Multiclass Support Vector Machine , 2012, Sensors.

[4]  Laurent Itti,et al.  shapeDTW: Shape Dynamic Time Warping , 2016, Pattern Recognit..

[5]  Michael Unser,et al.  Deep Convolutional Neural Network for Inverse Problems in Imaging , 2016, IEEE Transactions on Image Processing.

[6]  Alois Knoll,et al.  Motion Prediction of Virtual Patterns, Human Hand Motions, and a simplified Hand Manipulation Task with Hierarchical Temporal Memory , 2019, 2019 IEEE International Conference on Cyborg and Bionic Systems (CBS).

[7]  Bo Fu,et al.  Cascade Classifier Using Combination of Histograms of Oriented Gradients for Rapid Pedestrian Detection , 2013, J. Softw..

[8]  João Marcelo X. N. Teixeira,et al.  Evaluating Sign Language Recognition Using the Myo Armband , 2016, 2016 XVIII Symposium on Virtual and Augmented Reality (SVR).

[9]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[10]  Erik Marchi,et al.  Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection , 2017, Comput. Intell. Neurosci..

[11]  Nicolas D. Georganas,et al.  Real-Time Hand Gesture Detection and Recognition Using Bag-of-Features and Support Vector Machine Techniques , 2011, IEEE Transactions on Instrumentation and Measurement.

[12]  Marcel J. T. Reinders,et al.  Sign Language Recognition by Combining Statistical DTW and Independent Classification , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Soma Mitra,et al.  Vision based Hand Gesture Recognition using Dynamic Time Warping for Indian Sign Language , 2016, 2016 International Conference on Information Science (ICIS).

[14]  Thao Nguyen Thi Huong,et al.  Static hand gesture recognition for vietnamese sign language (VSL) using principle components analysis , 2015, 2015 International Conference on Communications, Management and Telecommunications (ComManTel).

[15]  Wen Gao,et al.  A Comparison Between Etymon- and Word-Based Chinese Sign Language Recognition Systems , 2005, Gesture Workshop.

[16]  Liangpei Zhang,et al.  Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification , 2017, Remote. Sens..

[17]  Shagan Sah,et al.  Large Scale Sign Language Interpretation , 2019, 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019).

[18]  Ali Karami,et al.  Persian sign language (PSL) recognition using wavelet transform and neural networks , 2011, Expert Syst. Appl..

[19]  Feng Jiang,et al.  Based on HMM and SVM multilayer architecture classifier for Chinese sign language recognition with large vocabulary , 2004, Third International Conference on Image and Graphics (ICIG'04).

[20]  Zhiwei Xu,et al.  Mortality and Disease Burden of Injuries from 2008 to 2017 in Anhui Province, China , 2020, BioMed research international.

[21]  Karush Suri,et al.  Convolutional Neural Network Array for Sign Language Recognition Using Wearable IMUs , 2019, 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN).

[22]  Wenhui Zhang,et al.  Research and implementation of sign language recognition method based on Kinect , 2016, 2016 2nd IEEE International Conference on Computer and Communications (ICCC).

[23]  Hongwu Yang,et al.  A Gesture-to-Emotional Speech Conversion by Combining Gesture Recognition and Facial Expression Recognition , 2018, 2018 First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia).

[24]  Li Lei,et al.  Design of data-glove and Chinese sign language recognition system based on ARM9 , 2015, 2015 12th IEEE International Conference on Electronic Measurement & Instruments (ICEMI).

[25]  Zhuowen Tu,et al.  Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Adnan Abid,et al.  A Comparison of Hardware Based Approaches for Sign Language Gesture Recognition Systems , 2019, 2019 International Conference on Innovative Computing (ICIC).

[27]  Carlos D. Martínez-Hinarejos,et al.  Sign Language Gesture Recognition Using HMM , 2017, IbPRIA.

[28]  Zhi-jie Liang,et al.  3D Convolutional Neural Networks for Dynamic Sign Language Recognition , 2018, Comput. J..

[29]  S. Majumder,et al.  Shape, texture and local movement hand gesture features for Indian Sign Language recognition , 2011, 3rd International Conference on Trendz in Information Sciences & Computing (TISC2011).

[30]  Tse-Yu Pan,et al.  Real-Time Sign Language Recognition in Complex Background Scene Based on a Hierarchical Clustering Classification Method , 2016, 2016 IEEE Second International Conference on Multimedia Big Data (BigMM).

[31]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Cheng-Yuan Liou,et al.  Autoencoder for words , 2014, Neurocomputing.

[33]  Wen Gao,et al.  A novel approach to automatically extracting basic units from Chinese sign language , 2004, ICPR 2004.

[34]  Shuihua Wang,et al.  Tea category identification based on optimal wavelet entropy and weighted k-Nearest Neighbors algorithm , 2018, Multimedia Tools and Applications.

[35]  Jian Sun,et al.  Instance-Aware Semantic Segmentation via Multi-task Network Cascades , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  Michal Kawulok.  Dynamic Skin Detection in Color Images for Sign Language Recognition , 2008, ICISP.

[37]  Wen Gao,et al.  Recognition of sign language subwords based on boosted hidden Markov models , 2005, ICMI '05.

[38]  Zaid Omar,et al.  A review of hand gesture and sign language recognition techniques , 2017, International Journal of Machine Learning and Cybernetics.

[39]  Yudong Zhang,et al.  Pathological brain detection based on wavelet entropy and Hu moment invariants. , 2015, Bio-medical materials and engineering.

[40]  Roberto Cipolla,et al.  Real-Time Adaptive Hand Motion Recognition Using a Sparse Bayesian Classifier , 2005, ICCV-HCI.

[41]  Jinye Peng,et al.  Chinese sign language recognition based on gray-level co-occurrence matrix and other multi-features fusion , 2009, 2009 4th IEEE Conference on Industrial Electronics and Applications.

[42]  Yu-Dong Zhang,et al.  Chinese Sign Language Fingerspelling via Six-Layer Convolutional Neural Network with Leaky Rectified Linear Units for Therapy and Rehabilitation , 2019, J. Medical Imaging Health Informatics.

[43]  Ana-Maria Cretu,et al.  Static and Dynamic Hand Gesture Recognition in Depth Data Using Dynamic Time Warping , 2016, IEEE Transactions on Instrumentation and Measurement.

[44]  J. L. Raheja,et al.  Indian sign language recognition using SVM , 2016, Pattern Recognition and Image Analysis.

[45]  Qing Zhu,et al.  Video-based Chinese sign language recognition using convolutional neural network , 2017, 2017 IEEE 9th International Conference on Communication Software and Networks (ICCSN).

[46]  Houqiang Li,et al.  Sign language recognition with long short-term memory , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[47]  Jiaying Liu,et al.  Adaptive Batch Normalization for practical domain adaptation , 2018, Pattern Recognit..

[48]  Wen Gao,et al.  A SRN/HMM system for signer-independent continuous sign language recognition , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[49]  Tao Li,et al.  Towards Computer-Aided Sign Language Recognition Technique: A Directional Review , 2019, 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC).

[50]  Wei Lu,et al.  Dynamic Hand Gesture Recognition With Leap Motion Controller , 2016, IEEE Signal Processing Letters.

[51]  Jian Sun,et al.  Identity Mappings in Deep Residual Networks , 2016, ECCV.

[52]  Samir I. Shaheen,et al.  Sign language recognition using a combination of new vision based features , 2011, Pattern Recognit. Lett..

[53]  Wen Gao,et al.  Large-Vocabulary Continuous Sign Language Recognition Based on Transition-Movement Models , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[54]  Qinghua Gao,et al.  A novel, co-located EMG-FMG-sensing wearable armband for hand gesture recognition , 2020 .

[55]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[56]  Hoda M. Onsi,et al.  Optimization of a Pre-Trained AlexNet Model for Detecting and Localizing Image Forgeries , 2020, Inf..

[57]  Aren Jansen,et al.  CNN architectures for large-scale audio classification , 2016, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[58]  Hermann Ney,et al.  Deep Sign: Hybrid CNN-HMM for Continuous Sign Language Recognition , 2016, BMVC.

[59]  Cleber Zanchettin,et al.  Gesture recognition: A review focusing on sign language in a mobile context , 2018, Expert Syst. Appl..

[60]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[61]  Vassilis Athitsos,et al.  A Survey on Sign Language Recognition Using Smartphones , 2017, PETRA.

[62]  Sherif Rashad,et al.  Comparing ANN, SVM, and HMM based Machine Learning Methods for American Sign Language Recognition using Wearable Motion Sensors , 2019, 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC).

[63]  Yang Quan,et al.  Chinese Sign Language Recognition for a Vision-Based Multi-features Classifier , 2008, 2008 International Symposium on Computer Science and Computational Technology.

[64]  Wen Gao,et al.  A Real-Time Large Vocabulary Recognition System for Chinese Sign Language , 2001, Gesture Workshop.

[65]  Yudong Zhang,et al.  Classification of Alzheimer Disease Based on Structural Magnetic Resonance Imaging by Kernel Support Vector Machine Decision Tree , 2014 .

[66]  S. L. Phung,et al.  A novel skin color model in YCbCr color space and its application to human face detection , 2002, Proceedings. International Conference on Image Processing.

[67]  Bo Hu,et al.  Fingerspelling Identification for Chinese Sign Language via AlexNet-Based Transfer Learning and Adam Optimizer , 2020, Sci. Program..

[68]  Xilin Chen,et al.  Isolated Sign Language Recognition with Grassmann Covariance Matrices , 2016, TACC.

[69]  Yudong Zhang,et al.  Detection of Alzheimer's disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC , 2015, Biomed. Signal Process. Control..

[70]  Hongxun Yao,et al.  Auto-encoder based dimensionality reduction , 2016, Neurocomputing.

[71]  Zhandong Liu,et al.  Chinese sign language recognition based on trajectory and hand shape features , 2016, 2016 Visual Communications and Image Processing (VCIP).

[72]  E. Kiran Kumar,et al.  Training CNNs for 3-D Sign Language Recognition With Color Texture Coded Joint Angular Displacement Maps , 2018, IEEE Signal Processing Letters.

[73]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[74]  Lidan Wang,et al.  The Application of Convolution Neural Networks in Sign Language Recognition , 2018, 2018 Ninth International Conference on Intelligent Control and Information Processing (ICICIP).

[75]  Arne Elofsson,et al.  Protein Contact Map Prediction Based on ResNet and DenseNet , 2020, BioMed research international.

[76]  Zhongfu Ye,et al.  A Novel Chinese Sign Language Recognition Method Based on Keyframe-Centered Clips , 2018, IEEE Signal Processing Letters.

[77]  Xiaoping Liu,et al.  Delineating urban functional areas with building-level social media data: A dynamic time warping (DTW) distance based k-medoids method , 2017 .

[78]  Casi Setianingsih,et al.  Traffic sign recognition application using speeded-up robust features (SURF) and support vector machine (SVM) based on android , 2017, 2017 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob).

[79]  Puja Padiya,et al.  Indian sign language to forecast text using leap motion sensor and RF classifier , 2016, 2016 Symposium on Colossal Data Analysis and Networking (CDAN).

[80]  T D Sajanraj,et al.  Indian Sign Language Numeral Recognition Using Region of Interest Convolutional Neural Network , 2018, 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT).

[81]  P. V. V. Kishore,et al.  4-Camera model for sign language recognition using elliptical fourier descriptors and ANN , 2015, 2015 International Conference on Signal Processing and Communication Engineering Systems.

[82]  Zoubin Ghahramani,et al.  A Theoretically Grounded Application of Dropout in Recurrent Neural Networks , 2015, NIPS.

[83]  Derek Ho,et al.  Glove-based hand gesture recognition sign language translator using capacitive touch sensor , 2016, 2016 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC).

[84]  Wei Shen,et al.  Research on convolutional neural network based on improved Relu piecewise activation function , 2018 .

[85]  Kittasil Silanon,et al.  Thai Finger-Spelling Recognition Using a Cascaded Classifier Based on Histogram of Orientation Gradient Features , 2017, Comput. Intell. Neurosci..

[86]  Ming Yang,et al.  Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning , 2016, Front. Comput. Neurosci..

[87]  Khaled Assaleh,et al.  Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode , 2015, IEEE Transactions on Human-Machine Systems.

[88]  Jie Huang,et al.  Video-based Sign Language Recognition without Temporal Segmentation , 2018, AAAI.

[89]  Nantinee Soodtoetong,et al.  The Efficiency of Sign Language Recognition using 3D Convolutional Neural Networks , 2018, 2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON).

[90]  Edwin C. Kan,et al.  A real-time spike classification method based on dynamic time warping for extracellular enteric neural recording with large waveform variability , 2016, Journal of Neuroscience Methods.

[91]  Zhongfu Ye,et al.  An Isolated Sign Language Recognition System Using RGB-D Sensor with Sparse Coding , 2014, 2014 IEEE 17th International Conference on Computational Science and Engineering.

[92]  Ali Farhadi,et al.  XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks , 2016, ECCV.

[93]  Taghi M. Khoshgoftaar,et al.  A survey of transfer learning , 2016, Journal of Big Data.

[94]  Houqiang Li,et al.  Iterative Alignment Network for Continuous Sign Language Recognition , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[95]  A. Bouzerdoum,et al.  A Bayesian approach to skin color classification in YCbCr color space , 2000, 2000 TENCON Proceedings. Intelligent Systems and Technologies for the New Millennium (Cat. No.00CH37119).

[96]  Daniel Rueckert,et al.  Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[97]  Emir Sokic,et al.  Phase preserving Fourier descriptor for shape-based image retrieval , 2016, Signal Process. Image Commun..

[98]  Houqiang Li,et al.  A new system for Chinese sign language recognition , 2015, 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP).

[99]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[100]  Ankush Mittal,et al.  K-nearest correlated neighbor classification for Indian sign language gesture recognition using feature fusion , 2016, 2016 International Conference on Computer Communication and Informatics (ICCCI).

[101]  Wen Gao,et al.  A vision-based sign language recognition system using tied-mixture density HMM , 2004, ICMI '04.

[102]  Juhan Nam,et al.  Multimodal Deep Learning , 2011, ICML.

[103]  Ayoub Al-Hamadi,et al.  A Hidden Markov Model-Based Isolated and Meaningful Hand Gesture Recognition , 2008 .

[104]  SchmidhuberJürgen.  Deep learning in neural networks , 2015 .

[105]  Kongqiao Wang,et al.  Automatic recognition of sign language subwords based on portable accelerometer and EMG sensors , 2010, ICMI-MLMI '10.

[106]  Mary Jane C. Samonte,et al.  Kinect-based sign language recognition of static and dynamic hand movements , 2017, International Conference on Graphic and Image Processing.

[107]  Surya Sumpeno,et al.  Classification of hand gesture in Indonesian sign language system using Naive Bayes , 2017, 2017 International Seminar on Sensors, Instrumentation, Measurement and Metrology (ISSIMM).

[108]  Ujwalla Gawande,et al.  Development of full duplex intelligent communication system for deaf and dumb people , 2017, 2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence.

[109]  Fei Yang,et al.  Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[110]  Eman El-Daydamony,et al.  Sign Language Hand Gesture Recognition Using Autoencoder and Support Vector Machine Classifiers , 2019, International journal of simulation: systems, science & technology.

[111]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[112]  Khaled Assaleh,et al.  Video-based signer-independent Arabic sign language recognition using hidden Markov models , 2009, Appl. Soft Comput..

[113]  Gaurav Kumar,et al.  A Detailed Review of Feature Extraction in Image Processing Systems , 2014, 2014 Fourth International Conference on Advanced Computing & Communication Technologies.

[114]  Shui-Hua Wang,et al.  An eight-layer convolutional neural network with stochastic pooling, batch normalization and dropout for fingerspelling recognition of Chinese sign language , 2019, Multimedia Tools and Applications.

[115]  Xiaoxu Li,et al.  Chinese Sign Language Recognition with Sequence to Sequence Learning , 2017, CCCV.

[116]  Pengwen Xiong,et al.  Dynamic Sign Language Recognition Based on Video Sequence With BLSTM-3D Residual Networks , 2019, IEEE Access.

[117]  Xilin Chen,et al.  Fast sign language recognition benefited from low rank approximation , 2015, 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[118]  Wen Gao,et al.  A Chinese sign language recognition system based on SOFM/SRN/HMM , 2004, Pattern Recognit..

[119]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[120]  Olivier Toubia,et al.  Extracting Features of Entertainment Products: A Guided Latent Dirichlet Allocation Approach Informed by the Psychology of Media Consumption , 2018, Journal of Marketing Research.

[121]  Hong Liu,et al.  Related HOG Features for Human Detection Using Cascaded Adaboost and SVM Classifiers , 2013, MMM.

[122]  Junding Sun,et al.  High Performance Multiple Sclerosis Classification by Data Augmentation and AlexNet Transfer Learning Model , 2019, J. Medical Imaging Health Informatics.

[123]  Zhaosong Zhu,et al.  Chinese Sign Language Identification via Wavelet Entropy and Support Vector Machine , 2019, ADMA.

[124]  Wen Gao,et al.  Signer-Independent Continuous Sign Language Recognition Based on SRN/HMM , 2001, Gesture Workshop.

[125]  Tae-Seong Kim,et al.  Hand number gesture recognition using recognized hand parts in depth images , 2014, Multimedia Tools and Applications.

[126]  Justin Salamon,et al.  Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification , 2016, IEEE Signal Processing Letters.

[127]  Kosin Chamnongthai,et al.  Signer-independence finger alphabet recognition using discrete wavelet transform and area level run lengths , 2016, J. Vis. Commun. Image Represent..

[128]  Kongqiao Wang,et al.  A Sign-Component-Based Framework for Chinese Sign Language Recognition Using Accelerometer and sEMG Data , 2012, IEEE Transactions on Biomedical Engineering.

[129]  Debi Prosad Dogra,et al.  A position and rotation invariant framework for sign language recognition (SLR) using Kinect , 2018, Multimedia Tools and Applications.

[130]  Ahmad Basheer Hassanat,et al.  Solving the Problem of the K Parameter in the KNN Classifier Using an Ensemble Learning Approach , 2014, ArXiv.

[131]  Ala addin I. Sidig,et al.  Arabic sign language recognition using vision and hand tracking features with HMM , 2019, Int. J. Intell. Syst. Technol. Appl..

[132]  Karen Livescu,et al.  Multitask training with unlabeled data for end-to-end sign language fingerspelling recognition , 2017, 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU).

[133]  Kongqiao Wang,et al.  A Framework for Hand Gesture Recognition Based on Accelerometer and EMG Sensors , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[134]  Wen Gao,et al.  HandTalker II: a Chinese sign language recognition and synthesis system , 2004, ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004..

[135]  Ge Yang,et al.  Real-Time Chinese Sign Language Recognition Based on Artificial Neural Networks* , 2019, 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[136]  Aboul Ella Hassanien,et al.  SIFT-Based Arabic Sign Language Recognition System , 2014, AECIA.

[137]  Xu Zhang,et al.  Random Forest-Based Recognition of Isolated Sign Language Subwords Using Data from Accelerometers and Surface Electromyographic Sensors , 2016, Sensors.

[138]  Xiang Cao,et al.  Chinese Sign Language Recognition Based on an Optimized Tree-Structure Framework , 2017, IEEE Journal of Biomedical and Health Informatics.

[139]  Xin Ma,et al.  End-to-End Residual Neural Network with Data Augmentation for Sign Language Recognition , 2019, 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC).

[140]  Wen Gao,et al.  A continuous Chinese sign language recognition system , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[141]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[142]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[143]  Chao Xie,et al.  Chinese sign language recognition with adaptive HMM , 2016, 2016 IEEE International Conference on Multimedia and Expo (ICME).

[144]  Subhransu Maji,et al.  Efficient Classification for Additive Kernel SVMs , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[145]  Wen Gao,et al.  Expanding Training Set for Chinese Sign Language Recognition , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[146]  Mohamed A. Deriche,et al.  Arabic sign language recognition using the leap motion controller , 2014, 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE).

[147]  Liangpei Zhang,et al.  A Multichannel Gray Level Co-Occurrence Matrix for Multi/Hyperspectral Image Texture Representation , 2014, Remote. Sens..

[148]  Haibo Wang,et al.  Chinese sign language recognition with 3D hand motion trajectories and depth images , 2014, Proceeding of the 11th World Congress on Intelligent Control and Automation.

[149]  Houqiang Li,et al.  Attention-Based 3D-CNNs for Large-Vocabulary Sign Language Recognition , 2019, IEEE Transactions on Circuits and Systems for Video Technology.