Age Progression

Libraries used

• OpenCV
• Dlib
• Numpy
• Pandas

Examples

How to run

1. Run the following commands

curl http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2 --output .\data\shape_predictor_68_face_landmarks.dat.bz2
mkdir data
tar -xvzf .\data\shape_predictor_68_face_landmarks.dat.bz2 -C .\data\shape_predictor_68_face_landmarks.dat

2. Download face dataset

• Download UTKFace.tar.gz from this link and move the extracted folder to data (final path: data/UTKFace)
• Download landmark lists from this link and move the txt files to data/landmark_list.

3. Check for file name errors

Filename guide

Label: [age]_[gender]_[race]_[date&time].jpg

• [age] is an integer from 0 to 116, indicating the age
• [gender] is either 0 (male) or 1 (female)
• [race] is an integer from 0 to 4, denoting White, Black, Asian, Indian, and Others (like Hispanic, Latino, Middle Eastern).
• [date&time] is in the format of yyyymmddHHMMSSFFF, showing the date and time an image was collected to UTKFace

Troubleshooting

• File names should be checked and matched with landmark_list.txt

References

• https://learnopencv.com/face-morph-using-opencv-cpp-python/
• https://learnopencv.com/delaunay-triangulation-and-voronoi-diagram-using-opencv-c-python/
• https://susanqq.github.io/UTKFace/