通过面部表情识别日语手语句型，准确率达96.05%。
Classification in Japanese Sign Language Based on Dynamic Facial Expressions
- 用神经网络分析面部特征，区分日语手语的肯定句与疑问句
- 在真实数据上实现96.05%的分类准确率
- 填补日语手语非手动特征研究空白，助聋人沟通
手语是通过手势和非手动标记表达的视觉语言。非手动标记包括面部表情和头部动作，且不同国家存在差异，因此需针对每种手语设计专门分析方法。然而，由于缺乏数据集，日语手语（JSL）识别研究仍较有限。开发同时考虑JSL的手部与非手动特征的识别模型，对实现与听障人士的精准顺畅交流至关重要。在JSL中，肯定句与疑问句等句型通过面部表情区分。本文提出一种聚焦面部表情的日语手语识别方法，利用神经网络分析面部特征并分类句型。实验表明，该方法有效，分类准确率达到96.05%。
原文摘要 · Abstract (English)
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%.
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