Single-Stage Joint Face Detection and Alignment
Jiankang Deng, Jia Guo, Stefanos P. Zafeiriou · 2019
In practice, there are huge demands to localize faces in images and videos under unconstrained pose variation, illumination change, severe occlusion and low resolution, which pose a great challenge to existing face detectors. This challenge report presents a single-stage joint face detection and alignment method. In detail, we employ feature pyramid network, single-stage detection, context modelling, multi-task learning and cascade regression to construct a practical face detector. On the Wider Face Hard validation subset, our single model achieves state-of-the-art performance (92.0% AP) compared with both academic and commercial face detectors for detecting unconstrained faces in cluttered scenes. In the Wider Face AND PERSON CHALLENGE 2019, our ensemble model achieves 56.66% average AP (runner-up) in the face detection track. To facilitate further research on the topic, the training code and models have been provided publicly available.