2018.10.14 Source code is available!!!

Progressive Calibration Networks (PCN) is an accurate rotation-invariant face detector running at real-time speed on CPU. This is an implementation for PCN.

Some detection results can be viewed in the following illustrations:

PCN is designed aiming for low time-cost. We compare PCN's speed with other rotation-invariant face detectors' on standard VGA images(640x480) with 40x40 minimum face size. The detectors run on a desktop computer with 3.4GHz CPU, GTX Titan X. The speed results together with the recall rate at 100 false positives on multi-oriented FDDB are shown in the following table. Detailed experiment settings can be found in our paper.

Set minimum size of faces to detect (size >= 20)

• detector.SetMinFaceSize(size);

Set scaling factor of image pyramid (1.4 <= factor <= 1.6)

• detector.SetImagePyramidScaleFactor(factor);

Set score threshold of detected faces (0 <= thresh1, thresh2, thresh3 <= 1)

• detector.SetScoreThresh(thresh1, thresh2, thresh3);

Smooth the face boxes or not (smooth = true or false, recommend using it on video to get stabler face boxes)

• detector.SetVideoSmooth(smooth);

See picture.cpp and video.cpp for details. If you want to reproduce the results on FDDB, please run fddb.cpp. You can rotate the images in FDDB to get FDDB-left, FDDB-right, and FDDB-down, then test PCN on them respectively.

Compile and run:

cd $PCN_ROOT/code
# You should set "CAFFEROOT" in lib.sh, compile.sh, and run.sh first. 
sh lib.sh
sh compile.sh picture/video/fddb
sh run.sh picture/video/fddb

• paper

• Linux
• Caffe
• OpenCV (2.4.10, or other compatible version)

This code is distributed under the BSD 2-Clause license.

If you find PCN useful in your research, please consider citing:

@inproceedings{shiCVPR18pcn,
    Author = {Xuepeng Shi and Shiguang Shan and Meina Kan and Shuzhe Wu and Xilin Chen},
    Title = {Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks},
    Booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    Year = {2018}
}

Xuepeng Shi, [email protected]