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Register a new userPose-based Sign Language Recognition using GCN and BERT
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Added by
Anirudh Tunga
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Sign language recognition (SLR) plays a crucial role in bridging the communication gap between the hearing and vocally impaired community and the rest of the society. Word-level sign language recognition (WSLR) is the first important step towards understanding and interpreting sign language. However, recognizing signs from videos is a challenging task as the meaning of a word depends on a combination of subtle body motions, hand configurations, and other movements. Recent pose-based architectures for WSLR either model both the spatial and temporal dependencies among the poses in different frames simultaneously or only model the temporal information without fully utilizing the spatial information. We tackle the problem of WSLR using a novel pose-based approach, which captures spatial and temporal information separately and performs late fusion. Our proposed architecture explicitly captures the spatial interactions in the video using a Graph Convolutional Network (GCN). The temporal dependencies between the frames are captured using Bidirectional Encoder Representations from Transformers (BERT). Experimental results on WLASL, a standard word-level sign language recognition dataset show that our model significantly outperforms the state-of-the-art on pose-based methods by achieving an improvement in the prediction accuracy by up to 5%.
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We propose a lightweight real-time sign language detection model, as we identify the need for such a case in videoconferencing. We extract optical flow features based on human pose estimation and, using a linear classifier, show these features are meaningful with an accuracy of 80%, evaluated on the DGS Corpus. Using a recurrent model directly on the input, we see improvements of up to 91% accuracy, while still working under 4ms. We describe a demo application to sign language detection in the browser in order to demonstrate its usage possibility in videoconferencing applications.
Inspired by the human ability to infer emotions from body language, we propose an automated framework for body language based emotion recognition starting from regular RGB videos. In collaboration with psychologists, we further extend the framework for psychiatric symptom prediction. Because a specific application domain of the proposed framework may only supply a limited amount of data, the framework is designed to work on a small training set and possess a good transferability. The proposed system in the first stage generates sequences of body language predictions based on human poses estimated from input videos. In the second stage, the predicted sequences are fed into a temporal network for emotion interpretation and psychiatric symptom prediction. We first validate the accuracy and transferability of the proposed body language recognition method on several public action recognition datasets. We then evaluate the framework on a proposed URMC dataset, which consists of conversations between a standardized patient and a behavioral health professional, along with expert annotations of body language, emotions, and potential psychiatric symptoms. The proposed framework outperforms other methods on the URMC dataset.
Sign language is commonly used by deaf or speech impaired people to communicate but requires significant effort to master. Sign Language Recognition (SLR) aims to bridge the gap between sign language users and others by recognizing signs from given videos. It is an essential yet challenging task since sign language is performed with the fast and complex movement of hand gestures, body posture, and even facial expressions. Recently, skeleton-based action recognition attracts increasing attention due to the independence between the subject and background variation. However, skeleton-based SLR is still under exploration due to the lack of annotations on hand keypoints. Some efforts have been made to use hand detectors with pose estimators to extract hand key points and learn to recognize sign language via Neural Networks, but none of them outperforms RGB-based methods. To this end, we propose a novel Skeleton Aware Multi-modal SLR framework (SAM-SLR) to take advantage of multi-modal information towards a higher recognition rate. Specifically, we propose a Sign Language Graph Convolution Network (SL-GCN) to model the embedded dynamics and a novel Separable Spatial-Temporal Convolution Network (SSTCN) to exploit skeleton features. RGB and depth modalities are also incorporated and assembled into our framework to provide global information that is complementary to the skeleton-based methods SL-GCN and SSTCN. As a result, SAM-SLR achieves the highest performance in both RGB (98.42%) and RGB-D (98.53%) tracks in 2021 Looking at People Large Scale Signer Independent Isolated SLR Challenge. Our code is available at https://github.com/jackyjsy/CVPR21Chal-SLR
Zero-Shot Learning (ZSL) has rapidly advanced in recent years. Towards overcoming the annotation bottleneck in the Sign Language Recognition (SLR), we explore the idea of Zero-Shot Sign Language Recognition (ZS-SLR) with no annotated visual examples, by leveraging their textual descriptions. In this way, we propose a multi-modal Zero-Shot Sign Language Recognition (ZS-SLR) model harnessing from the complementary capabilities of deep features fused with the skeleton-based ones. A Transformer-based model along with a C3D model is used for hand detection and deep features extraction, respectively. To make a trade-off between the dimensionality of the skeletonbased and deep features, we use an Auto-Encoder (AE) on top of the Long Short Term Memory (LSTM) network. Finally, a semantic space is used to map the visual features to the lingual embedding of the class labels, achieved via the Bidirectional Encoder Representations from Transformers (BERT) model. Results on four large-scale datasets, RKS-PERSIANSIGN, First-Person, ASLVID, and isoGD, show the superiority of the proposed model compared to state-of-the-art alternatives in ZS-SLR.
This paper presents a system which can recognise hand poses & gestures from the Indian Sign Language (ISL) in real-time using grid-based features. This system attempts to bridge the communication gap between the hearing and speech impaired and the rest of the society. The existing solutions either provide relatively low accuracy or do not work in real-time. This system provides good results on both the parameters. It can identify 33 hand poses and some gestures from the ISL. Sign Language is captured from a smartphone camera and its frames are transmitted to a remote server for processing. The use of any external hardware (such as gloves or the Microsoft Kinect sensor) is avoided, making it user-friendly. Techniques such as Face detection, Object stabilisation and Skin Colour Segmentation are used for hand detection and tracking. The image is further subjected to a Grid-based Feature Extraction technique which represents the hands pose in the form of a Feature Vector. Hand poses are then classified using the k-Nearest Neighbours algorithm. On the other hand, for gesture classification, the motion and intermediate hand poses observation sequences are fed to Hidden Markov Model chains corresponding to the 12 pre-selected gestures defined in ISL. Using this methodology, the system is able to achieve an accuracy of 99.7% for static hand poses, and an accuracy of 97.23% for gesture recognition.
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