基于多尺度时空卷积的唇语识别方法

叶鸿; 危劲松; 贾兆红; 郑辉; 梁栋; 唐俊

doi:10.11999/JEIT240161

基于多尺度时空卷积的唇语识别方法

doi: 10.11999/JEIT240161

叶鸿¹,
危劲松¹,
贾兆红¹,
郑辉^1, ,,
梁栋¹,
唐俊²

1.
安徽大学互联网学院合肥 230039
2.
安徽大学电子信息工程学院合肥 230601

基金项目: 国家自然科学基金(71971002, 62273001)，安徽省自然科学基金(2108085QA35)，安徽省重点研究与开发计划(202004a07020050)，安徽省科技重大专项(202003A06020016)，安徽省高校优秀科研创新团队(2022AH010005)

详细信息

作者简介:
叶鸿：男，硕士生导师，研究方向为深度学习、人工智能、体系架构优化、并行计算

危劲松：男，硕士生，研究方向为计算机视觉

贾兆红：女，教授，研究方向为人工智能、决策支持、多目标优化

郑辉：男，讲师，研究方向为多模态感知、计算机视觉

梁栋：男，教授，研究方向为计算机视觉与模式识别、信号处理与智能系统

唐俊：男，教授，研究方向为计算机视觉与机器学习

通讯作者:
郑辉　huizheng@ahu.edu.cn

中图分类号: TN911.73; TP391.41
计量
- 文章访问数: 71
- HTML全文浏览量: 25
- PDF下载量: 9
- 被引次数: 0
出版历程
- 收稿日期: 2024-03-12
- 修回日期: 2024-09-10
- 网络出版日期: 2024-09-16

Lipreading Method Based on Multi-Scale Spatiotemporal Convolution

1.
School of Internet, Anhui University, Hefei 230039, China
2.
School of Electronic and Information Engineering, Anhui University, Hefei 230601, China

Funds: The National Natural Science Foundation of China (71971002, 62273001), The Provincial Natural Science Foundation of Anhui (2108085QA35), Anhui Provincial Key Research and Development Project (202004a07020050), Anhui Provincial Major Science and Technology Project (202003A06020016), The Excellent Research and Innovation Teams in Anhui Province’s Universities (2022AH010005)

摘要

摘要: 现有的唇语识别模型大多采用将单层的3维卷积与2维卷积神经网络结合的方式，从唇语视频序列中挖掘出时空联合特征。然而，由于单层的3维卷积不能很好地提取时间信息，同时2维卷积神经网络对细粒度的唇语特征的挖掘能力有限，该文提出一种多尺度唇语识别网络(MS-LipNet)以改善唇语识别任务。该文在Res2Net网络中，采用3维时空卷积替代传统的2维卷积以更好地提取时空联合特征，同时提出时空坐标注意力模块，使网络关注于任务相关的重要区域特征。在LRW和LRW-1000数据集上进行实验，验证了所提方法的有效性。
- 唇语识别 /
- 多尺度时空卷积网络 /
- Res2Net /
- 时空坐标注意力 /
- 数据增强
Abstract: Most of the existing lipreading models use a combination of single-layer 3D convolution and 2D convolutional neural networks to extract spatio-temporal joint features from lip video sequences. However, due to the limitations of single-layer 3D convolutions in capturing temporal information and the restricted capability of 2D convolutional neural networks in exploring fine-grained lipreading features, a Multi-Scale Lipreading Network (MS-LipNet) is proposed to improve lip reading tasks. In this paper, 3D spatio-temporal convolution is used to replace traditional two-dimensional convolution in Res2Net network to better extract spatio-temporal joint features, and a spatio-temporal coordinate attention module is proposed to make the network focus on task-related important regional features. The effectiveness of the proposed method was verified through experiments conducted on the LRW and LRW-1000 datasets.
- Lipreading /
- Multi-scale spatiotemporal convolutional network /
- Res2Net /
- Spatiotemporal coordinate attention /
- Data augmentation

HTML全文

图 1 MS-LipNet整体框架

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图 2 STCA注意力结构图

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图 3 STCA子模块结构图

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图 4 ST-Res2Net结构图

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图 5 模型的显著性图对比

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表 1 不同方法在LRW和LRW-1000数据集上的识别准确率对比(%)

方法	LRW	LRW-1000
Two-Stream ResNet18+BiLSTM^[18]	84.07	–
2×ResNet18+BiGRU^[19]	84.13	41.93
ResNet18+3×BiGRU+MI^[30]	84.41	38.79
ResNet18+MS-TCN^[9]	85.30	41.40
SE-ResNet18+BiGRU^[13]	85.00	48.00
3D-ResNet18+BiGRU+TSM^[20]	86.23	44.60
ResNet18+HPConv+self-attention^[19]	86.83	–
WPCL+APFF^[31]	88.30	49.40
ResNet-18+DC-TCN^[10]	88.36	43.65
2DCNN+BiGRU+Lip Segmentation^[32]	90.38	–
ResNet18+DC-TCN+TimeMask^[33]	90.40	–
MS-LipNet	91.56	50.68

下载: 导出CSV

表 2 MS-LipNet网络不同组件的消融实验结果(%)

模型	数据增强		注意力模块	LRW	LRW-1000
模型	Mixup	Cutout	STCA	LRW	LRW-1000
MS-LipNet	×	×	×	90.95	50.12
	√	×	×	91.06	50.40
	×	√	×	91.01	50.42
	×	×	√	91.21	50.25
	√	√	×	91.18	50.06
	√	×	√	91.39	50.56
	×	√	√	91.48	50.50
	√	√	√	91.56	50.68

下载: 导出CSV

表 3 Cutout的不同取值对实验结果的影响(%)

n_holes	length	LRW	LRW-1000
0	0	91.39	50.50
1	11	91.41	50.51
1	22	91.44	50.53
1	44	91.42	50.55
2	11	91.49	50.54
2	22	91.56	50.68
2	44	91.43	50.58
3	11	91.50	50.65
3	22	91.37	50.59
3	44	90.72	50.51

下载: 导出CSV

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