This paper investigates the feasibility of using lightweight body landmark detection for the recognition of isolated signs in Brazilian Sign Language (LIBRAS). Although the skeleton-based approach by Alves et al. (2024) enabled substantial improvements in recognition performance, the use of OpenPose for landmark extraction hindered time performance. In a preliminary investigation, we observed that simply replacing OpenPose with the lightweight MediaPipe, while improving processing speed, significantly reduced accuracy. To overcome this limitation, we explored landmark subset selection strategies aimed at optimizing recognition performance. Experimental results showed that a proper landmark subset achieves comparable or superior performance to state-of-the-art methods while reducing processing time by more than 5X compared to Alves et al. (2024). As an additional contribution, we demonstrated that spline-based imputation effectively mitigates missing landmark issues, leading to substantial accuracy gains. These findings highlight that careful landmark selection, combined with simple imputation techniques, enables efficient and accurate isolated sign recognition, paving the way for scalable Sign Language Recognition systems.

翻译：本文研究了使用轻量级身体关键点检测技术识别巴西手语（LIBRAS）中孤立手语的可行性。尽管Alves等人（2024）提出的基于骨架的方法显著提升了识别性能，但使用OpenPose进行关键点提取影响了时间效率。在初步研究中，我们观察到仅将OpenPose替换为轻量级MediaPipe虽能提升处理速度，但会显著降低准确率。为克服此局限，我们探索了旨在优化识别性能的关键点子集选择策略。实验结果表明，恰当的关键点子集在实现与先进方法相当或更优性能的同时，处理时间较Alves等人（2024）的方法减少5倍以上。作为额外贡献，我们证明了基于样条插值的填补方法能有效缓解关键点缺失问题，从而显著提升准确率。这些发现表明，精细的关键点选择结合简单的填补技术，能够实现高效准确的孤立手语识别，为可扩展的手语识别系统开辟了道路。