| 唇语识别的深度学习方法综述 |
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| 引用本文： | 马金林,朱艳彬,马自萍,巩元文,陈德光,刘宇灏. 唇语识别的深度学习方法综述[J]. 计算机工程与应用, 2021, 57(24): 61-73. DOI: 10.3778/j.issn.1002-8331.2109-0196 |
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| 作者姓名： | 马金林 朱艳彬 马自萍 巩元文 陈德光 刘宇灏 |
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| 作者单位： | 1.北方民族大学 计算机科学与工程学院，银川 7500212.图像图形智能信息处理国家民委重点实验室，银川 7500213.北方民族大学 数学与信息科学学院，银川 750021 |
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| 摘 要： | 随着深度学习的不断发展，唇语识别领域的研究取得了重大进展，涌现了许多唇语识别的深度学习算法。依据识别对象的连续性，将唇语识别分为孤立唇语识别和连续唇语识别，并对各识别任务的深度学习方法进行了详细和深入的分析总结。从孤立唇语识别的深度学习方法和连续唇语识别的深度方法两个方面介绍了主流唇语识别方法，并对各方法的优缺点和性能进行比较；对不同数据集下代表性方法的特点和性能进行比较，对两类方法的优缺点和适用范围进行阐述；讨论了唇语识别方法存在的问题和挑战，并对唇语识别方法的研究趋势进行了展望。
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| 关 键 词： | 唇语识别 深度学习 卷积神经网络 注意力机制 |
Review of Deep Learning Methods for Lip Recognition |
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| MA Jinlin,ZHU Yanbin,MA Ziping,GONG Yuanwen,CHEN Deguang,LIU Yuhao. Review of Deep Learning Methods for Lip Recognition[J]. Computer Engineering and Applications, 2021, 57(24): 61-73. DOI: 10.3778/j.issn.1002-8331.2109-0196 |
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| Authors: | MA Jinlin ZHU Yanbin MA Ziping GONG Yuanwen CHEN Deguang LIU Yuhao |
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| Affiliation: | 1.School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China2.Key Laboratory for Intelligent Processing of Computer Images and Graphics of the State Ethnic Affairs Commission of PRC, Yinchuan 750021, China3.School of Mathematics and Information Science, North Minzu University, Yinchuan 750021, China |
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| Abstract: | With the continuous development of deep learning, significant progress has been made in the field of lip recognition, and many deep learning algorithms for lip recognition have emerged. According to the continuity of the recognition object, this paper divides lip recognition into isolated lip recognition and continuous lip recognition, analyzes and summarizes the deep learning methods of each recognition task in detail and in-depth. Firstly, it introduces the mainstream lip recognition methods from the two aspects of the deep learning method of isolated lip recognition and the deep method of continuous lip recognition, and compares the advantages, disadvantages and performance of each method. Secondly, the characteristics and performance of representative methods for different data sets are compared, and the advantages, disadvantages and application scope of the two methods are expounded. Finally, the problems and challenges of lip recognition methods are discussed, and the research trend of lip recognition methods is prospected. |
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| Keywords: | lip recognition deep learning convolutional neural networks attentional mechanisms |
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