Real-Time Rotation-Invariant Face Detection with
Progressive Calibration Networks
Abstract
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
360? RIP angles. Such designs lead to a real-time rotationinvariant 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.