Sign Languages are expressed through hand and upper body gestures as well as
facial expressions. Therefore, Sign Language Recognition (SLR) needs to focus
on all such cues. Previous work uses hand-crafted mechanisms or network
aggregation to extract the different cue features, to increase SLR performance.
This is slow and involves complicated architectures. We propose a more
straightforward approach that focuses on training separate cue models
specializing on the dominant hand, hands, face, and upper body regions. We
compare the performance of 3D Convolutional Neural Network (CNN) models
specializing in these regions, combine them through score-level fusion, and use
the weighted alternative. Our experimental results have shown the effectiveness
of mixed convolutional models. Their fusion yields up to 19% accuracy
improvement over the baseline using the full upper body. Furthermore, we
include a discussion for fusion settings, which can help future work on Sign
Language Translation (SLT).

通过 3D 卷积神经网络及得分融合方法，我们训练了针对主导手、双手、面部及上半身区域的不同模型，并比较它们的性能表现。实验结果表明，这种混合卷积模型的融合能够比使用完整上半身的基线提高高达 19% 的准确率，并提供有助于未来手语翻译的融合设置讨论。