ChildPredictor

This is the official webpage of the paper "ChildPredictor: A Child Face Prediction Framework with Disentangled Learning", accepted to IEEE TMM, 2022

🚀 🚀 🚀 News:

Apr. 15, 2022: We release the trained models with samples for ChildPredictor.
Mar. 31, 2022: The paper is accepted by the IEEE Transactions on Multimedia.
Feb. 8, 2022: We release the code for ChildPredictor. We are considerring to release the original data of the collected FF-Database.

1 FF-Database

We will release the larger-than-ever kinship dataset (FF-Database). Currently, we are asking for legal advice as soon as possible due to the privacy issue.

The data collection pipeline is shown as follows:

Some families are shown as follows:

2 Results on Real Families

The generated results on the collected FF-Database:

The generated results on other datasets:

The disentangled learning analysis is as:

The ablation study is as:

3 Implementation

3.1 File Structure

Some files are not included in the current implementation since they are too large. The network architectures can be found in the code folder.

code
│
└───baby_model_pool (not provided)
│   └───attgan
│   │   │   attgan_without_claloss_baby.pth
│   │   │   attgan_without_ganloss_celeba_baby.pth
│   │   │   attgan_without_ganloss_claloss_celeba_baby.pth
│   │   │   ...
│   └───inverse
│   │   │   Inverse_ProGAN_GAN_ACGAN_start-with-code.pth
│   │   │   Inverse_ProGAN_GAN_MSGAN_ACGAN_start-with-code.pth
│   │   │   Inverse_ProGAN_GAN_MSGAN_ACGAN_start-with-image.pth
│   │   │   ...
│   └───mapping
│   │   └───Mapping_Xencoder_full_ProGAN_GAN_MSGAN_ACGAN_deepArch_multi-gt_v4
│   │   │   │   MappingNet_Batchsize_32_Epoch_298.pth
│   │   └───Mapping_Xencoder_full_ProGAN_GAN_deepArch_multi-gt_v4
│   │   │   │   MappingNet_Batchsize_32_Epoch_298.pth
│   │   └───Mapping_Xencoder_wo-class_ProGAN_GAN_MSGAN_deepArch_multi-gt_v4
│   │   │   │   MappingNet_Batchsize_32_Epoch_298.pth
│   │   │   ...
│   └───ProGAN-ckp
│   │   │   ProGAN_pt_mixtureData_GAN.pth
│   │   │   ProGAN_pt_mixtureData_GAN_ACGAN.pth
│   │   │   ProGAN_pt_mixtureData_GAN_MSGAN.pth
│   │   │   ProGAN_pt_mixtureData_GAN_MSGAN_ACGAN.pth
│   │   │   ...
│
└───babyinverse (Ey)
│   │   ...
|
└───babymapping_1219 (T)
│   │   ...
│
└───Datasets
│   │   ...
│
└───ProGAN (Gy)
│   │   ...
│
└───AttGAN (please refer to AttGAN official webpage)
│   │   ...
│

3.2 Required Libraries

The following packages are needed to be installed:

pytorch==1.1.0
torchvision==0.3.0
tensorboardx
pyyaml
tqdm
easydict

3.3 Testing a Real Face

First, download the pre-trained models and unzip them under code folder: https://portland-my.sharepoint.com/:f:/g/personal/yzzhao2-c_my_cityu_edu_hk/EoJ0dSnBBgNPnJtCGz108aMBexjNuPU4aF7ePBCzP_yEcQ?e=fkHLuF

Then, you can test some validation samples (we have already put some examples under the code/babymapping_1219 folder):

cd code
cd babymapping_1219
python main.py

If you want to change the input images, see lines 38-39 of validation.yaml: https://github.com/zhaoyuzhi/ChildPredictor/blob/main/code/babymapping_1219/yaml/yaml/validation.yaml

3.4 Training

Currently, we do not release the full codes for training due to privacy issue.

3.5 Build Your Own Dataset

Please refer to code_FFDatabase_collection.

4 Network Architectures

5 Reference

@article{zhao2022childpredictor,
  title={ChildPredictor: A Child Face Prediction Framework with Disentangled Learning},
  author={Zhao, Yuzhi and Po, Lai-Man and Wang, Xuehui and Yan, Qiong and Shen, Wei and Zhang, Yujia and Liu, Wei and Wong Chun-Kit and Pang, Chiu-Sing and Ou, Weifeng and Yu, Wing-Yin and Liu, Buhua},
  journal={IEEE Transactions on Multimedia},
  year={2022}
}

6 Some Related Works

Zaman, Ishtiak and Crandall, David. Genetic-GAN: Synthesizing Images Between Two Domains by Genetic Crossover. European Conference on Computer Vision Workshops, 312--326, 2020.
Gao, Pengyu and Robinson, Joseph and Zhu, Jiaxuan and Xia, Chao and Shao, MIng and Xia, Siyu. DNA-Net: Age and Gender Aware Kin Face Synthesizer. IEEE International Conference on Multimedia and Expo (ICME), 2021.
Robinson, Joseph Peter and Khan, Zaid and Yin, Yu and Shao, Ming and Fu, Yun. Families in wild multimedia (FIW MM): A multimodal database for recognizing kinship. IEEE Transactions on Multimedia, 2021.

wmpscc/ChildPredictor