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Worked on AFLW2000-3D dataset which is a dataset of 2000 images. The regression model of predicting the 3 angles (pitch - yaw - roll) of head pose estimation was XGboost Regressor.

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YomnaAhmed97/Head-Pose-Estimation

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Head-Pose-Estimation

In this project I've worked on AFLW2000-3D dataset which is a dataset of 2000 images that have been annotated with image-level 68-point 3D facial landmarks, with various head poses for humans and animations. By using mediapipe to extract faces landmarks which are 468 points in 3D, but we used x-axis & y-axis. The regression model of predicting the 3 angles (pitch - yaw - roll) of head pose estimation was XGboost Regressor.

Data

You can download from http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/Database/AFLW2000-3D.zip This data contains different pictures for human faces with different poses.

Steps:

  1. Preparing data for model training ( we used MediaPipe and CV2 libraries for extracting points and for face detection from pictures).
  2. Spliting the data to training, validation and testing.
  3. Using regression model.
  4. Detecting a random pic to validate the model from dataset.

Conc:

Humans:

tryfromerror (1)

Ataf-pose-estimation

Animation:

hunter

For more information about mediapipe:

https://google.github.io/mediapipe/solutions/face_mesh.html

About

Worked on AFLW2000-3D dataset which is a dataset of 2000 images. The regression model of predicting the 3 angles (pitch - yaw - roll) of head pose estimation was XGboost Regressor.

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