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Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks
Rotation-invariant face detection, i.e. detecting faces with arbitrary
rotation-in-plane (RIP) angles, is widely required in unconstrained
applications but still remains as a challenging task, due to the large
variations of face appearances. Most existing methods compromise with speed or
accuracy to handle the large RIP variations. To address this problem more
efficiently, we propose Progressive Calibration Networks (PCN) to perform
rotation-invariant face detection in a coarse-to-fine manner. PCN consists of
three stages, each of which not only distinguishes the faces from non-faces,
but also calibrates the RIP orientation of each face candidate to upright
progressively. By dividing the calibration process into several progressive
steps and only predicting coarse orientations in early stages, PCN can achieve
precise and fast calibration. By performing binary classification of face vs.
non-face with gradually decreasing RIP ranges, PCN can accurately detect faces
with full RIP angles. Such designs lead to a real-time
rotation-invariant face detector. The experiments on multi-oriented FDDB and a
challenging subset of WIDER FACE containing rotated faces in the wild show that
our PCN achieves quite promising performance.Comment: Accepted by The IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2018). Code: \url{https://github.com/Jack-CV/PCN
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