  Sign language recognition (SLR) is a challenging problem, involving complex
manual features, i.e., hand gestures, and fine-grained non-manual features
(NMFs), i.e., facial expression, mouth shapes, etc. Although manual features
are dominant, non-manual features also play an important role in the expression
of a sign word. Specifically, many sign words convey different meanings due to
non-manual features, even though they share the same hand gestures. This
ambiguity introduces great challenges in the recognition of sign words. To
tackle the above issue, we propose a simple yet effective architecture called
Global-local Enhancement Network (GLE-Net), including two mutually promoted
streams towards different crucial aspects of SLR. Of the two streams, one
captures the global contextual relationship, while the other stream captures
the discriminative fine-grained cues. Moreover, due to the lack of datasets
explicitly focusing on this kind of features, we introduce the first
non-manual-features-aware isolated Chinese sign language dataset~(NMFs-CSL)
with a total vocabulary size of 1,067 sign words in daily life. Extensive
experiments on NMFs-CSL and SLR500 datasets demonstrate the effectiveness of
our method.
