  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).
