Face Detection and Segmentation Based on Improved Mask R-CNN
Deep convolutional neural networks have been successfully applied to face detection recently. Despite making remarkable progress, most of the existing detection methods only localize each face using a bounding box, which cannot segment each face from the background image simultaneously. To overcome...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2020/9242917 |
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| _version_ | 1850177684130234368 |
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| author | Kaihan Lin Huimin Zhao Jujian Lv Canyao Li Xiaoyong Liu Rongjun Chen Ruoyan Zhao |
| author_facet | Kaihan Lin Huimin Zhao Jujian Lv Canyao Li Xiaoyong Liu Rongjun Chen Ruoyan Zhao |
| author_sort | Kaihan Lin |
| collection | DOAJ |
| description | Deep convolutional neural networks have been successfully applied to face detection recently. Despite making remarkable progress, most of the existing detection methods only localize each face using a bounding box, which cannot segment each face from the background image simultaneously. To overcome this drawback, we present a face detection and segmentation method based on improved Mask R-CNN, named G-Mask, which incorporates face detection and segmentation into one framework aiming to obtain more fine-grained information of face. Specifically, in this proposed method, ResNet-101 is utilized to extract features, RPN is used to generate RoIs, and RoIAlign faithfully preserves the exact spatial locations to generate binary mask through Fully Convolution Network (FCN). Furthermore, Generalized Intersection over Union (GIoU) is used as the bounding box loss function to improve the detection accuracy. Compared with Faster R-CNN, Mask R-CNN, and Multitask Cascade CNN, the proposed G-Mask method has achieved promising results on FDDB, AFW, and WIDER FACE benchmarks. |
| format | Article |
| id | doaj-art-c39896de9ddc44fc97df1f3fbb43e543 |
| institution | OA Journals |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-c39896de9ddc44fc97df1f3fbb43e5432025-08-20T02:18:55ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/92429179242917Face Detection and Segmentation Based on Improved Mask R-CNNKaihan Lin0Huimin Zhao1Jujian Lv2Canyao Li3Xiaoyong Liu4Rongjun Chen5Ruoyan Zhao6School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaSchool of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaSchool of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaSchool of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaSchool of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaSchool of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaSchool of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaDeep convolutional neural networks have been successfully applied to face detection recently. Despite making remarkable progress, most of the existing detection methods only localize each face using a bounding box, which cannot segment each face from the background image simultaneously. To overcome this drawback, we present a face detection and segmentation method based on improved Mask R-CNN, named G-Mask, which incorporates face detection and segmentation into one framework aiming to obtain more fine-grained information of face. Specifically, in this proposed method, ResNet-101 is utilized to extract features, RPN is used to generate RoIs, and RoIAlign faithfully preserves the exact spatial locations to generate binary mask through Fully Convolution Network (FCN). Furthermore, Generalized Intersection over Union (GIoU) is used as the bounding box loss function to improve the detection accuracy. Compared with Faster R-CNN, Mask R-CNN, and Multitask Cascade CNN, the proposed G-Mask method has achieved promising results on FDDB, AFW, and WIDER FACE benchmarks.http://dx.doi.org/10.1155/2020/9242917 |
| spellingShingle | Kaihan Lin Huimin Zhao Jujian Lv Canyao Li Xiaoyong Liu Rongjun Chen Ruoyan Zhao Face Detection and Segmentation Based on Improved Mask R-CNN Discrete Dynamics in Nature and Society |
| title | Face Detection and Segmentation Based on Improved Mask R-CNN |
| title_full | Face Detection and Segmentation Based on Improved Mask R-CNN |
| title_fullStr | Face Detection and Segmentation Based on Improved Mask R-CNN |
| title_full_unstemmed | Face Detection and Segmentation Based on Improved Mask R-CNN |
| title_short | Face Detection and Segmentation Based on Improved Mask R-CNN |
| title_sort | face detection and segmentation based on improved mask r cnn |
| url | http://dx.doi.org/10.1155/2020/9242917 |
| work_keys_str_mv | AT kaihanlin facedetectionandsegmentationbasedonimprovedmaskrcnn AT huiminzhao facedetectionandsegmentationbasedonimprovedmaskrcnn AT jujianlv facedetectionandsegmentationbasedonimprovedmaskrcnn AT canyaoli facedetectionandsegmentationbasedonimprovedmaskrcnn AT xiaoyongliu facedetectionandsegmentationbasedonimprovedmaskrcnn AT rongjunchen facedetectionandsegmentationbasedonimprovedmaskrcnn AT ruoyanzhao facedetectionandsegmentationbasedonimprovedmaskrcnn |