Face Detection and Segmentation Based on Improved Mask R-CNN

• Published in: Discrete dynamics in nature and society Vol. 2020; no. 2020; pp. 1 - 11
• Main Authors: Zhao, Ruoyan, Fu, Hui, Li, Canyao, Lv, Jujian, Zhao, Huimin, Lin, Kaihan, Chen, Rongjun
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Accuracy; Artificial neural networks; Cable television broadcasting industry; Convolution; Datasets; Face recognition; Feature extraction; Image detection; Image segmentation; Methods; Neural networks; Researchers; United States
• ISSN: 1026-0226; 1607-887X
• DOI: 10.1155/2020/9242917

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.