基于注意模型的美国手语拼写识别

Amruta E Kabade, P. Desai, S. C, Shankar G
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引用次数: 1

摘要

手语识别是一个复杂的手势识别问题,因为手势具有快速和高度的协同运动。本研究的重点是占美国手语(ASL) 35%的手指拼写识别任务。手指拼写识别一个字母一个字母的单词。指拼是指对专业术语、实词、专有名词等没有指定手语符号的单词进行手语。在我们提出的ASL手指拼写识别工作中,我们考虑了芝加哥野生数据集,该数据集由不同照明、照明条件下(在野生环境中)捕获的遮挡和图像组成。利用Lucas-Kanade算法获取光流,生成先验,利用人脸感兴趣区域技术对图像进行调整和裁剪,得到感兴趣区域。视觉注意机制对ROI的响应是迭代的。在Imagenet上进行预训练的ResNet用于提取空间特征。采用连接时间分类(CTC)的Bi-LSTM网络进行符号预测。在chicagoofswild数据集上提供57%的准确率用于指纹识别任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
American Sign Language Fingerspelling Recognition using Attention Model
Sign Language Recognition(SLR) is a complex gesture recognition problem because of the quick and highly coarticulated motion involved in gestures. This research work focuses on Fingerspelling recognition task, which constitutes 35% of the American Sign Language (ASL). Fingerspelling identifies the word letter by letter. Fingerspelling is used for signing the words which do not have designated ASL signs such as technical terms, content words and proper nouns. In our proposed work for ASL Fingerspelling recognition, we consider ChicagoFSWild dataset which consists of occlusions and images captured in varying illuminations, lighting conditions (in the wild environments). The optical flow is obtained from Lucas-Kanade algorithm, prior is generated, images are resized and cropped with face-roi technique to get the region of interest (ROI). The visual attention mechanism attends to the ROI iteratively. ResNet, pretrained on Imagenet is used for the extraction of spatial features. The Bi-LSTM network with Connectionist Temporal Classification (CTC) is used to predict the sign. It provides the accuracy of 57% on ChicagoFSWild dataset for Fingerspelling recognition task.
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