Rotation-Invariant Face Detection with Multi-task Progressive Calibration Networks
| Autor: | Fa-Yuan Liu, Jie Liu, Patrick S. P. Wang, Li-Fang Zhou, Gu Yu, Tian-Yu Xu |
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| Rok vydání: | 2020 |
| Předmět: |
Landmark
Computer science business.industry Detector Multi-task learning 020206 networking & telecommunications 02 engineering and technology Invariant (physics) Convolutional neural network 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence Performance improvement business Face detection Feature learning |
| Zdroj: | Pattern Recognition and Artificial Intelligence ISBN: 9783030598297 ICPRAI |
| Popis: | Rotation-invariant face detection (RIFD) aims to detect faces with arbitrary rotation-in-plane (RIP) on both images and video sequences, which is challenging because of large appearance variations of rotated faces under unconstrained scenarios. The problem becomes more formidable when the speed and memory efficiency of the detectors are taken into account. To solve this problem, we propose a Multi-Task Progressive Calibration Networks (MTPCN), which not only enjoy the natural advantage of explicit geometric structure representation, but also retain important cue to guide the feature learning for precise calibration. More concretely, our framework leverages a cascaded architecture with three stages of carefully designed convolutional neural networks to predict face and landmark location with gradually decreasing RIP ranges. In addition, we propose a novel loss by further integrating geometric information into penalization, which is much more reasonable than simply measuring the differences of training samples equally. MTPCN achieves significant performance improvement on the multi-oriented FDDB dataset and the rotation WIDER face dataset. Extensive experiments are performed, and demonstrate the effectiveness of our method for each of three tasks. |
| Databáze: | OpenAIRE |
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