Global-local Enhancement Network for NMFs-aware Sign Language Recognition

• URL: http://arxiv.org/abs/2008.10428v2
• Date: Mon, 16 Aug 2021 03:38:16 GMT
• Title: Global-local Enhancement Network for NMFs-aware Sign Language Recognition
• Authors: Hezhen Hu, Wengang Zhou, Junfu Pu, Houqiang Li
• Abstract summary: We propose a simple yet effective architecture called Global-local Enhancement Network (GLE-Net) Of the two streams, one captures the global contextual relationship, while the other stream captures the discriminative fine-grained cues. We introduce the first non-manual-features-aware isolated Chinese sign language dataset with a total vocabulary size of 1,067 sign words in daily life.
• Score: 135.30357113518127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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