MTCNet: Multi-task collaboration network for rotation-invariance face detection
作者：
Highlights：
• We propose a novel multi-task collaboration network (MTCNet) to deal with faces with large rotation faces by cooperation between face detection and face alignment, which avoids the semantic ambiguity among RIP angle groups well.
• We incorporate the angle information to optimize the loss function of face alignment to improve the efficiency of model convergence.
• We introduce the contextual information to enhance the quality of feature maps for mining those faces with serious rotation change from unconstrained scenarios.
• Extensive experiments are conducted to demonstrate the effectiveness of the proposed MTCNet on FDDB and WIDER FACE.
摘要
•We propose a novel multi-task collaboration network (MTCNet) to deal with faces with large rotation faces by cooperation between face detection and face alignment, which avoids the semantic ambiguity among RIP angle groups well.•We incorporate the angle information to optimize the loss function of face alignment to improve the efficiency of model convergence.•We introduce the contextual information to enhance the quality of feature maps for mining those faces with serious rotation change from unconstrained scenarios.•Extensive experiments are conducted to demonstrate the effectiveness of the proposed MTCNet on FDDB and WIDER FACE.
论文关键词：Rotation-invariant face detection,Face alignment,Multi-task learning
论文评审过程：Received 8 May 2021, Revised 30 October 2021, Accepted 6 November 2021, Available online 9 November 2021, Version of Record 28 February 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.108425
