Age Progression/Regression by Conditional Adversarial Autoencoder (CAAE)

Utilising the TensorFlow implementation (this repo is forked) of the algorithm in the paper Age Progression/Regression by Conditional Adversarial Autoencoder.

Pre-requisites

Revamped for the following usage:

• Python 3.5.2 (original was 2.7x)
• Scipy 1.0.0
• Tensorflow-gpu 1.14 (original used 1.7.0)

Datasets

• https://github.com/JingchunCheng/All-Age-Faces-Dataset (aligned faces)

Training

$ python main.py

The training process has been tested on Google Cloud Platform NVIDIA Tesla P100 NVIDIA. The training time for 50 epochs on 13k images is roughly 3h.

Custom Training

$ python main.py
    --dataset		default 'asian-faces'. Please put your own dataset in ../data
    --savedir		default 'save'. Please use a meaningful name, e.g., save_init_model.
    --epoch		default 50.
    --use_trained_model	default True. If use a trained model, savedir specifies the model name. 
    --use_init_model	default True. If load the trained model failed, use the init model save in ./init_model 

Testing

$ python main.py --is_train False --testdir your_image_dir --savedir save

A demo of training process

Files

• FaceAging.py is a class that builds and initializes the model, and implements training and testing related stuff
• ops.py consists of functions called FaceAging.py to implement options of convolution, deconvolution, fully connection, leaky ReLU, load and save images.
• main.py demonstrates FaceAging.py.

References

Zhifei Zhang, Yang Song, and Hairong Qi. "Age Progression/Regression by Conditional Adversarial Autoencoder." IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

@inproceedings{zhang2017age,
  title={Age Progression/Regression by Conditional Adversarial Autoencoder},
  author={Zhang, Zhifei and Song, Yang and Qi, Hairong},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017}
}