anshu0612 / human_attributes_prediction_in_privacy_conditions

ACM MM'21 (Oral): Official implementation of the Context-guided Human Attributes Prediction Network (CHAPNet) introduced in Human Attributes Prediction Under Privacy-preserving Conditions.

Human Attributes Prediction Under Privacy-preserving Conditions (Accepted at ACM Multimedia 2021 -- Oral)

This repository is the official implementation of the Context-guided Human Attributes Prediction Network (CHAPNet) introduced in Human Attributes Prediction Under Privacy-preserving Conditions.

Main paper | Supplementary material | Project page | Related blog

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Left: We built the CHAPNet guided by out human findings. We utilized the psychophysics observations for emotion, age, and gender prediction to design CHAPNet, an end-to-end multi-tasking human attributes classification deep learning model. The advantage of basing our model design on human behaviour is that it makes the network architecture explainable.

Right: Qualitative results with intact face images by CHAPNet trained on only face obfuscated images (from DPaC dataset).

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Content

• Setup
  • Dependencies
  • Expected directory structure of the data
  • Diversity in Context and People Dataset
  • Pose generation
• Usage
  • Training Samples
  • Evaluation Sample
• Contact
• References
• License

Setup

Dependencies

The experiments have been conducted under:

• PyTorch: 1.7.1
• Python: 3.6.9

To install requirements:

pip3 install -r requirements.txt

Expected directory structure of the data

├── data
│   ├── data.json
│   ├── train_test_split.json
│   ├── privacy
│   |    ├── eye 
|   |    |     ├── images 
|   |    |     |     ├── 1.jpg
|   |    |     |     .....
|   |    |     ├── pose
|   |    |     |     ├── 1_0.npy #pose map for data["1"]["persons"][0]
|   |    |     |     ├── 1_1.npy #pose map for data["1"]["persons"][1]
|   |    |     |     ├── 1_2.npy #pose map for data["1"]["persons"][2]
|   |    |     |     .....
│   |    ├── lower
|   |    |     ├── images 
|   |    |     ├── pose
│   |    ├── face
|   |    |     ├── images 
|   |    |     ├── pose
│   |    ├── head 
|   |    |     ├── images 
|   |    |     ├── pose
│   ├── intact
│   |    ├── images
│   |    ├── pose
├──models
├──utils
......

Diversity in Context and People Dataset

Our Diversity in Context and People Dataset (DPaC) dataset with images containing obfuscated faces are available online. The dataset with images of intact faces and other face obfuscations can be provided upon request.

Pose generation

The pose folder in the above directory structure expects .npy files of pose guided heatmaps generated for each image.

Steps to generate them:

1. Get the cropped targets images using the body_bb.
2. Generate a .json file containing pose landmarks for each of the cropped targets using OpenPose library.
3. Generate heatmaps .npy files for each of the cropped targets by running the file generate_heatmaps.py in the utils folder. Note: name the .npy files as suggested in the above directory structure.

Sample to run generate_heatmaps.py:

python3 generate_heatmaps.py --cropped_targets_imgs_path "/targets/" --pose_data_path '/pose_landmarks.json' --save_path '/pose/' 

Usage

Training Samples

#training with default settings (optionally you can set --gpu_device if GPU is available)
python3 train.py 

#training on images with head obfuscation of only the targets    
python3 train.py --ob_face_region head --ob_people TO 

#training on images with all the detected faces obfuscated (default ob_face_region = 'face')
python3 train.py --ob_people AO 

#training on images with all the detected faces' eyes regions obfuscated (default ob_people  = 'AO')
python3 train.py --ob_face_region eye 

Arguments

Argument	Description	Default
num_epochs	Set the number of epochs	40
batch_size	Set the batch size	16
lr	Set the initial learning rate	0.01
weight_decay	Set the weight decay in the range [0, 1]	5e-4
ob_face_region	Set the face region to obfuscated. Valid values are { None, eye, lower, face, head}	None
ob_people	Set whether to obfuscate all the detected faces (AO) or only the targets (TO). Valid values are { None, TO, AO }	None
gpu_device	Set the GPU device to train the model on	0

Evaluation Sample

# optionally you can set --gpu_device if GPU is available
python3 test.py --cp_path "/cp_DPAC_face_AO/29.pth"

Arguments

Argument	Description	Default
cp_path	Set the path to the checkpoint	-
gpu_device	Set the GPU device to train the model on	0

Citation

If this repository was useful in your research, please cite our paper:

@inproceedings{singh2021human,
  title={Human Attributes Prediction under Privacy-preserving Conditions},
  author={Singh, Anshu and Fan, Shaojing and Kankanhalli, Mohan},
  booktitle={Proceedings of the 29th ACM International Conference on Multimedia},
  pages={4698--4706},
  year={2021}
}

Contact

If you have any questions, feel free to open an issue or directly contact me via: [email protected]

References

Pose-guided target branch inspired by: Miao, Jiaxu, et al. "Pose-guided feature alignment for occluded person re-identification." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019.

License

MIT license, as found in the LICENSE file.