Child Face Generation
This is the second and final architecture in the workflow of the dual-architecture system designed for predicting child facial features from parental facial features.
The two stage system is:
- (1) Extract an encoding of the parental features and find a mid-point encoding
- (2) Find a probabilistic mapping from this encoding to child features
For this project, we use BEGAN coupled with spherical interpolation for Stage I and Pix2Pix cGAN for Stage II.
See the full report here.
Requirements
To install the requirements, an Anaconda environment is highly recommended:
conda create --name <env> --file requirements_conda.txt
Additionally, a requirements.txt file for pip installation is created. This method is not tested or recommended:
pip install -r requirements.txt
Dataset
The dataset in the format required can be downloaded here.
Before training the model, move this file to the data/processed folder.
See the Parent Child in the Wild (PCW) section below for more information on the dataset.
Running 🏃
Activate the environment:
source activate <env>
Then navigate to src/models and run:
python main.py --backend tensorflow --dset cupidV --nb_epoch 400 --generator deconv --img_dim 128 32 32
This is to train the model for 400 epochs with the deconvolutional method for a 128x128 and a 32x32 patch size.
Trained Model
The full trained model can be downloaded here.
Experiments & Representative Output
In the following images, the rows from top to bottom are: input interpolated parent facial features, predicted child facial features, ground truth. Below is a representative output from the deconvolutional method:
The upsampling method was also tested, but returned subjectively worse results. Below is a representative output:
Parent-Child in The Wild (PCW)
No suitable datasets containing a mother-father-child relationship for facial images was found. As such, via scraping and validation methods described in the report, this dataset was created.
Example family photo for creating the dataset:
Families are grouped and all facial images are cropped, aligned, and annotated with predicted gender and age. 766 families are available.
Download the raw data here.