Hopenet is an accurate and easy to use head pose estimation network. Models have been trained on the 300W-LP dataset and have been tested on real data with good qualitative performance. For details about the method and quantitative results please check the CVPR Workshop paper.
This implementation is based on Tencent NCNN a high-performance neural network inference computing framework optimized for mobile platforms.
Original PyTorch implementation: https://github.com/natanielruiz/deep-head-pose
# build with cmake
cd hopenet_ncnn
mkdir build && cd build
cmake ..
make
# run test with
# ./hopenet_test [path_to_image] [optional_gpu_device_id]
(hopenet) nils@europa:~/hopenet_ncnn/build$ ./hopenet_test ../terminator.jpg
[0 GeForce GTX 1060 6GB] queueC=2[8] queueG=0[16] queueT=1[2]
[0 GeForce GTX 1060 6GB] buglssc=0 bugsbn1=0 buglbia=0 bugihfa=0
[0 GeForce GTX 1060 6GB] fp16p=1 fp16s=1 fp16a=0 int8s=1 int8a=1
with GPU_SUPPORT, selected gpu_device: 0
[X] 0 DISCRETEIf you find Hopenet useful in your research please cite:
@InProceedings{Ruiz_2018_CVPR_Workshops,
author = {Ruiz, Nataniel and Chong, Eunji and Rehg, James M.},
title = {Fine-Grained Head Pose Estimation Without Keypoints},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}