Sign language visual recognition from continuous multi-modal streams is still one of the most challenging fields. Recent advances in human actions recognition are exploiting the ascension of GPU-based learning from massive data, and are getting closer to human-like performances. They are then prone to creating interactive services for the deaf and hearing-impaired communities. A population that is expected to grow considerably in the years to come. This paper aims at reviewing the human actions recognition literature with the sign-language visual understanding as a scope. The methods analyzed will be mainly organized according to the different types of unimodal inputs exploited, their relative multi-modal combinations and pipeline steps. In each section, we will detail and compare the related datasets, approaches then distinguish the still open contribution paths suitable for the creation of sign language related services. Special attention will be paid to the approaches and commercial solutions handling facial expressions and continuous signing.

中文翻译：

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手语识别的视觉方法：基于模态的评论

来自连续多模态流的手语视觉识别仍然是最具挑战性的领域之一。人类动作识别的最新进展正在利用基于 GPU 的海量数据学习的提升，并且越来越接近于类人的表现。然后，他们倾向于为聋人和听障社区创建互动服务。预计未来几年人口将大幅增长。本文旨在回顾以手语视觉理解为范围的人类行为识别文献。分析的方法将主要根据所利用的不同类型的单模态输入、它们的相关多模态组合和管道步骤来组织。在每个部分，我们将详细和比较相关的数据集，方法然后区分适合创建手语相关服务的仍然开放的贡献路径。将特别关注处理面部表情和连续签名的方法和商业解决方案。