Classification in Japanese Sign Language Based on Dynamic Facial Expressions
This work addresses the need for precise communication with deaf individuals by developing a specialized recognition model for JSL, which is incremental due to limited prior research and datasets.
The paper tackled the problem of recognizing Japanese Sign Language (JSL) sentence types by focusing on dynamic facial expressions, achieving a classification accuracy of 96.05%.
Sign language is a visual language expressed through hand movements and non-manual markers. Non-manual markers include facial expressions and head movements. These expressions vary across different nations. Therefore, specialized analysis methods for each sign language are necessary. However, research on Japanese Sign Language (JSL) recognition is limited due to a lack of datasets. The development of recognition models that consider both manual and non-manual features of JSL is crucial for precise and smooth communication with deaf individuals. In JSL, sentence types such as affirmative statements and questions are distinguished by facial expressions. In this paper, we propose a JSL recognition method that focuses on facial expressions. Our proposed method utilizes a neural network to analyze facial features and classify sentence types. Through the experiments, we confirm our method's effectiveness by achieving a classification accuracy of 96.05%.