Patch Attention Layer of Embedding Handcrafted Features in CNN for Facial Expression Recognition
文献类型：期刊论文
| 作者 | Liang, Xingcan1,2; Xu, Linsen1,3 ; Liu, Jinfu1; Liu, Zhipeng1,2; Cheng, Gaoxin1,2; Xu, Jiajun2; Liu, Lei2
|
| 刊名 | SENSORS
 |
| 出版日期 | 2021-02-01 |
| 卷号 | 21 |
| 关键词 | facial expression recognition patch attention shallow feature feature extraction facial representation convolutional layer |
| DOI | 10.3390/s21030833 |
| 通讯作者 | Xu, Linsen(lsxu@iamt.ac.cn) |
| 英文摘要 | Recognizing facial expression has attracted much more attention due to its broad range of applications in human-computer interaction systems. Although facial representation is crucial to final recognition accuracy, traditional handcrafted representations only reflect shallow characteristics and it is uncertain whether the convolutional layer can extract better ones. In addition, the policy that weights are shared across a whole image is improper for structured face images. To overcome such limitations, a novel method based on patches of interest, the Patch Attention Layer (PAL) of embedding handcrafted features, is proposed to learn the local shallow facial features of each patch on face images. Firstly, a handcrafted feature, Gabor surface feature (GSF), is extracted by convolving the input face image with a set of predefined Gabor filters. Secondly, the generated feature is segmented as nonoverlapped patches that can capture local shallow features by the strategy of using different local patches with different filters. Then, the weighted shallow features are fed into the remaining convolutional layers to capture high-level features. Our method can be carried out directly on a static image without facial landmark information, and the preprocessing step is very simple. Experiments on four databases show that our method achieved very competitive performance (Extended Cohn-Kanade database (CK+): 98.93%; Oulu-CASIA: 97.57%; Japanese Female Facial Expressions database (JAFFE): 93.38%; and RAF-DB: 86.8%) compared to other state-of-the-art methods. |
| 资助项目 | National Key R&D Program of China[2017YFB1303200] ; Science and Technology Major Project of Anhui Province[17030901034] ; Special Project for Frontier Leading Basic Technology of Jiangsu Province[BK20192004] |
| WOS研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000615517100001 |
| 资助机构 | National Key R&D Program of China ; Science and Technology Major Project of Anhui Province ; Special Project for Frontier Leading Basic Technology of Jiangsu Province |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/119648]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Xu, Linsen |
| 作者单位 | 1.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China 2.Univ Sci & Technol China, Hefei 230026, Peoples R China 3.Anhui Prov Key Lab Biomimet Sensing & Adv Robot T, Hefei 230031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liang, Xingcan,Xu, Linsen,Liu, Jinfu,et al. Patch Attention Layer of Embedding Handcrafted Features in CNN for Facial Expression Recognition[J]. SENSORS,2021,21. |
| APA | Liang, Xingcan.,Xu, Linsen.,Liu, Jinfu.,Liu, Zhipeng.,Cheng, Gaoxin.,...&Liu, Lei.(2021).Patch Attention Layer of Embedding Handcrafted Features in CNN for Facial Expression Recognition.SENSORS,21. |
| MLA | Liang, Xingcan,et al."Patch Attention Layer of Embedding Handcrafted Features in CNN for Facial Expression Recognition".SENSORS 21(2021). |
入库方式： OAI收割
来源：合肥物质科学研究院
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