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OS: win10 / VS2017
CPU: i3-1115G4
Target device : GPU
model:
Face Detection model: face-detection-adas-0001 => work fine
Head Pose Estimation model: head-pose-estimation-adas-0001 => work fine
Facial Landmarks Estimation model: facial-landmarks-35-adas-0002 => work fine
Open/Closed Eye Estimation model: open-closed-eye => not work
( transfer from /openvino_models/public/open-closed-eye-0001/open-closed-eye.onnx )
Gaze Estimation model: gaze-estimation-adas-0002 = > not work
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Hi Brian,
Thank you for waiting and apologies for the delay.
I’ve just validated Gaze Estimation Demo using the following parameters, and am glad to inform you that the demo is working fine.
- OpenVINO Version: 2021.2
- Devices : CPU and GPU
- OS : Windows 10
- Input Video : head-pose-face-detection-female-and-male.mp4 video (available at https://github.com/intel-iot-devkit/sample-videos)
The steps to execute the demo are given here:
1. Download all the required models using Model Downloader:
· gaze-estimation-adas-0002
· face-detection-retail-0004
· face-detection-adas-0001
· head-pose-estimation-adas-0001
· facial-landmarks-35-adas-0002
· open-closed-eye-0001
2. Convert open-closed-eye.onnx model to IR format using Model Optimizer, as follows:
python mo.py --input_model "C:\Program Files (x86)\Intel\openvino_2021\deployment_tools\open_model_zoo\tools\downloader\public\open-closed-eye-0001\open-closed-eye.onnx" --mean_values [127.0,127.0,127.0] --scale_values [255,255,255] --output 19
The conversion argument is available here:
3. Run the demo as follows:
python <path_to_demo_applications_binaries>\gaze_estimation_demo.exe -i <path_to_video>\input_video.mp4 -m <path_to_model>\gaze-estimation-adas-0002.xml -m_fd <path_to_model>\face-detection-retail-0004.xml -m_hp <path_to_model>\head-pose-estimation-adas-0001.xml -m_lm <path_to_model>\facial-landmarks-35-adas-0002.xml -m_es <path_to_model>\open-closed-eye.xml
For target device GPU, you can add the arguments as follows:
-d GPU, -d_fd GPU, -d_hp GPU, -d_lm GPU, and -d_es GPU
Regards,
Munesh
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Hi Bryan,
Thanks for reaching out to us. We are investigating this issue, and will get back to you shortly.
Regards,
Munesh
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Hi Brian,
Thank you for waiting and apologies for the delay.
I’ve just validated Gaze Estimation Demo using the following parameters, and am glad to inform you that the demo is working fine.
- OpenVINO Version: 2021.2
- Devices : CPU and GPU
- OS : Windows 10
- Input Video : head-pose-face-detection-female-and-male.mp4 video (available at https://github.com/intel-iot-devkit/sample-videos)
The steps to execute the demo are given here:
1. Download all the required models using Model Downloader:
· gaze-estimation-adas-0002
· face-detection-retail-0004
· face-detection-adas-0001
· head-pose-estimation-adas-0001
· facial-landmarks-35-adas-0002
· open-closed-eye-0001
2. Convert open-closed-eye.onnx model to IR format using Model Optimizer, as follows:
python mo.py --input_model "C:\Program Files (x86)\Intel\openvino_2021\deployment_tools\open_model_zoo\tools\downloader\public\open-closed-eye-0001\open-closed-eye.onnx" --mean_values [127.0,127.0,127.0] --scale_values [255,255,255] --output 19
The conversion argument is available here:
3. Run the demo as follows:
python <path_to_demo_applications_binaries>\gaze_estimation_demo.exe -i <path_to_video>\input_video.mp4 -m <path_to_model>\gaze-estimation-adas-0002.xml -m_fd <path_to_model>\face-detection-retail-0004.xml -m_hp <path_to_model>\head-pose-estimation-adas-0001.xml -m_lm <path_to_model>\facial-landmarks-35-adas-0002.xml -m_es <path_to_model>\open-closed-eye.xml
For target device GPU, you can add the arguments as follows:
-d GPU, -d_fd GPU, -d_hp GPU, -d_lm GPU, and -d_es GPU
Regards,
Munesh
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Hi Bryan,
This thread will no longer be monitored since this issue has been resolved. If you need any additional information from Intel, please submit a new question.
Regards,
Munesh

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