Skeleton Aware Multi-modal Sign Language Recognition (2103.08833v5)

Abstract: 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

• The paper proposes SAM-SLR, a novel multi-modal approach combining skeleton-based graphs with traditional RGB and depth data for enhanced sign language recognition.
• It employs a Sign Language Graph Convolution Network and Separable Spatial-Temporal Convolution Network to effectively model intricate spatio-temporal dynamics of human gestures.
• Experimental results show superior performance with recognition accuracies above 98% on both RGB and RGB-D tasks in a leading SLR challenge.

Analysis of "Skeleton Aware Multi-modal Sign Language Recognition"

This paper introduces an integrated multi-modal framework for Sign Language Recognition (SLR) that combines skeleton-based and conventional visual methods, aiming to improve performance in recognizing complex sign language. SLR is inherently challenging due to the need to accurately decipher rapid and intricate gestures, body movements, and facial expressions. While existing methods predominantly utilize RGB data, the innovative approach in this paper, named Skeleton Aware Multi-modal SLR (SAM-SLR), leverages multi-modal data, anchoring on whole-body skeleton data, to enhance comprehension of sign language.

Key Contributions

• Skeleton-based Approach: The authors propose a novel Sign Language Graph Convolution Network (SL-GCN) to model human skeletal dynamics effectively. They construct a graph using whole-body keypoints, significantly enhancing the ability to capture motion intricacies crucial for interpreting sign language.
• Spatio-temporal Dynamics: A Separable Spatial-Temporal Convolution Network (SSTCN) is introduced to delve into the finer details of skeleton features, allowing the model to focus on both spatial and temporal aspects of gestures simultaneously.
• Multi-modality Fusion: The SAM-SLR framework synthesizes information from multiple modalities—namely skeleton, RGB, and depth data. The fusion of these data streams enables the model to achieve superior performance by capturing complementary information that single-modal systems might overlook.

Experimental Results

The framework achieves noteworthy results on the 2021 Looking at People Large Scale Signer Independent Isolated SLR Challenge. It recorded a recognition accuracy of 98.42% on RGB and 98.53% on RGB-D tasks, representing the highest performance metrics in the competition. This validates the effectiveness of integrating multi-modal and skeleton-based approaches in SLR.

Technical Details and Methodology

• Graph Construction: The authors perform graph reduction to condense the human-body keypoints from 133 to a more manageable 27, focusing on critical joints that convey significant motion information necessary for SLR.
• Multi-stream SL-GCN: The framework harnesses multiple data streams including joint, bone, and motion information for joint and bones, processed through the SL-GCN. This multi-stream approach ensures rich feature extraction, enabling the network to distinguish nuanced differences between signs.
• Baseline Comparison: The skeleton-based SL-GCN surpassed traditional RGB-based methods in accuracy and computational efficiency, demonstrating the advantage of this innovative graph-based approach.

Implications and Future Directions

The convergence of skeleton-based and traditional methods presents a compelling direction for future SLR developments. By utilizing whole-body keypoints, the framework provides robust adaptability against varying environmental conditions, which is a common challenge in visual recognition tasks. The integration of depth and flow information also indicates potential for further expansions into other action recognition tasks beyond sign language.

Future work could delve into the exploration of even finer skeletal movements using high-fidelity sensors or further enhancements in neural architecture to minimize computational overhead while retaining high accuracy. Additionally, considering cultural and linguistic variations in sign language could present another dimension for research, enriching models' understanding across different demographics.

In summary, this paper underscores the potential of leveraging diverse data streams and innovative neural network architectures to advance the field of sign language recognition. The presented work sets a new benchmark in SLR, paving the way for more inclusive communication technologies.