Efficient Isolated Sign Language Recognition Using Skeleton Image Representation

Konsep Inti

The proposed method leverages skeleton image representation and 2D convolutional neural networks to efficiently recognize isolated signs in Brazilian Sign Language, outperforming state-of-the-art approaches.

Abstrak

The paper introduces a method for recognizing isolated signs in Brazilian Sign Language (LIBRAS) using skeleton image representation and 2D convolutional neural networks. The key steps are:

Landmark extraction: Body, hand, and facial landmarks are extracted from the input video frames using OpenPose.
Image encoding: The extracted landmarks are converted into a 2D image that encodes both spatial and temporal information using the Skeleton-DML algorithm.
Classification: The encoded image is fed into a ResNet-18 based CNN model for sign classification.

The proposed method was evaluated on two widely recognized LIBRAS datasets - MINDS-Libras and LIBRAS-UFOP. It achieved state-of-the-art performance, surpassing a complex multimodal 3D CNN approach in both accuracy and efficiency.
The ablation study showed that the data augmentation had a modest impact, while the frame uniformization mechanism improved performance on the more challenging LIBRAS-UFOP dataset. The authors also discussed the time efficiency limitation due to the landmark extraction step, which is a common challenge across methods relying on OpenPose.
Overall, the proposed approach demonstrates the effectiveness of skeleton image representation and 2D CNNs for isolated sign language recognition, providing a simpler yet efficient alternative to complex multimodal 3D CNN architectures.

Statistik

The MINDS-Libras dataset contains 1,158 video sequences of 20 distinct LIBRAS signs performed by 12 signers.
The LIBRAS-UFOP dataset contains 56 distinct LIBRAS signs performed by 5 signers, with 8 to 16 repetitions per sign.

Kutipan

"Effective communication is paramount for the inclusion of deaf individuals in society."
"To communicate with non-signers whether hearing or deaf, signing deaf individuals often resort to less natural and slower forms of communication, such as lip reading and/or written messages."
"Overcoming communication barriers necessitates, among other measures, technologies that facilitate SL learning and enable bidirectional translation: from signs to speech/text and vice versa."

Wawasan Utama Disaring Dari

Enhancing Brazilian Sign Language Recognition through Skeleton Image Representation

by Carl... pada arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19148.pdf

Pertanyaan yang Lebih Dalam

How could the proposed method be extended to handle continuous sign language recognition, where the input is a sequence of connected signs

To extend the proposed method for continuous sign language recognition, where signs are connected in a sequence, several modifications and enhancements can be implemented. One approach could involve incorporating a temporal modeling component into the existing framework. This could be achieved by integrating recurrent neural networks (RNNs) or transformers to capture the sequential nature of connected signs. By processing the input as a time series of sign representations, the model can learn the dependencies between consecutive signs and improve the overall recognition accuracy. Additionally, attention mechanisms can be employed to focus on relevant parts of the input sequence, aiding in the recognition of complex sign transitions. By adapting the current CNN-based architecture to handle sequential data and incorporating temporal modeling techniques, the system can be effectively extended to handle continuous sign language recognition.

What other types of visual data, beyond skeleton information, could be explored to further improve the recognition performance, especially on more challenging datasets like LIBRAS-UFOP

Beyond skeleton information, several other types of visual data could be explored to enhance sign language recognition performance, especially on challenging datasets like LIBRAS-UFOP. One promising avenue is the integration of depth maps obtained from depth-sensing cameras or sensors like the Microsoft Kinect. Depth information provides additional spatial cues that can improve the accuracy of hand and body pose estimation, crucial for sign language recognition. Furthermore, incorporating motion flow data, which captures the movement patterns of different body parts over time, can enhance the model's ability to distinguish between subtle sign variations. Thermal imaging, which detects heat signatures, could also be utilized to complement RGB data and provide additional context for sign recognition. By combining multiple modalities of visual data, the model can leverage complementary information to improve performance, especially on complex and nuanced sign language datasets.

What are the potential applications of efficient isolated sign language recognition systems, and how could they be integrated into real-world assistive technologies for the deaf and hard-of-hearing community

Efficient isolated sign language recognition systems have a wide range of potential applications in real-world assistive technologies for the deaf and hard-of-hearing community. One key application is the development of real-time sign language translation tools that can facilitate communication between signers and non-signers. These systems can be integrated into smartphones, tablets, and other devices to enable seamless and instant translation of sign language into spoken or written language. Additionally, isolated sign language recognition systems can be utilized in educational settings to create interactive learning platforms for sign language learners. By providing real-time feedback on sign accuracy and proficiency, these systems can enhance the learning experience and promote inclusivity in educational environments. Furthermore, isolated sign language recognition can be integrated into smart home devices and wearable technologies to enable hands-free communication for deaf individuals, allowing them to control devices, access information, and interact with their surroundings more effectively. By leveraging the capabilities of isolated sign language recognition systems, assistive technologies can empower deaf and hard-of-hearing individuals to communicate more efficiently and participate fully in various aspects of daily life.