AnyFace++: Deep Multi-Task Multi-Domain Learning for Efficient Face AI (Preprint)

Installation requirements

Clone the repository and install all necessary packages. Please ensure that Python>=3.8 with PyTorch>=1.8.

git clone https://github.com/IS2AI/AnyFacePP.git
cd AnyFacePP
pip install ultralytics

The following datasets were used to train, validate, and test the models.

Dataset	Link
Facial Expression Recognition 2013	https://www.kaggle.com/competitions/challenges-in-representation-learning-facial-expression-recognition-challenge/data
AffectNet	http://mohammadmahoor.com/affectnet/
IMDB	https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/
UTKFace	https://susanqq.github.io/UTKFace/
Adience	https://talhassner.github.io/home/projects/Adience/Adience-data.html
MegaAge	http://mmlab.ie.cuhk.edu.hk/projects/MegaAge/
MegaAge Asian	http://mmlab.ie.cuhk.edu.hk/projects/MegaAge/
AFAD Dataset	https://afad-dataset.github.io/
AgeDB	https://complexity.cecs.ucf.edu/agedb/
FairFace	https://github.com/joojs/fairface
Uniform Age and Gender Dataset (UAGD)	https://github.com/clarkong/UAGD
FG-NET	https://yanweifu.github.io/FG_NET_data/
RAF-DB (Real-world Affective Faces)	http://www.whdeng.cn/raf/model1.html
Wider Face	http://shuoyang1213.me/WIDERFACE/
AnimalWeb	https://fdmaproject.wordpress.com/author/fdmaproject/
iCartoonFace	https://github.com/luxiangju-PersonAI/iCartoonFace#dataset
TFW	https://github.com/IS2AI/TFW#downloading-the-dataset

Preprocessing Step

Use notebooks in the main directory to pre-process the corresponding datasets.

The preprocessed datasets are saved in dataset/ directory. For each dataset, images are stored in dataset/<dataset_name>/images/ and the corresponding labels are stored in dataset/dataset_name/labels/ and in dataset/<dataset_name>/labels_eval/. Labels are saved in .txt files, where each .txt file has the same filename as corresponding image.

Annotations in dataset/<dataset_name>/labels/ follow the format used for training YOLOv8Face models:

• face_type x_center y_center width height x1 y1 x2 y2 x3 y3 x4 y4 x5 y5 gender age emotion
• face_type' represents the type of face: 0 - human, 1 - animal, 2 - cartoon.
• x1,y1,...,x5,y5 correspond to the coordinates of the left eye, right eye, nose top, left mouth corner, and right mouth corner.
• gender denotes the gender of the person: 1 - male, 0 - female, 2 - unsure.
• age indicates the age of the person.
• emotion specifies one of the 7 basic emotions (0 - angry, 1 - happy, 2 - fear, 3 - sad, 4 - surprise, 5 - disgust, 6 - neutral, -2 - unsure). All coordinates are normalized to values between 0 and 1. If a face lacks any of the labels, -1 is used in place of the missing values.

Training Step

Inference

Download the most accurate model, YOLOv8, from Google Drive and save it.

python3 inference.py

You can specify parameters in code:

path_to_model="last.pt" path_to_image="ex/RAFdb_test_0003.jpg" #if you want use image from the Internet, replace path with None image_url=None #if you want use image from the Internet, replace None with URL threshold_bboxes=0.3

In case of using our work in your research, please cite this paper

Tomiris Rakhimzhanova, Askat Kuzdeuov, Huseyin Atakan Varol. AnyFace++: Deep Multi-Task Multi-Domain Learning for Efficient Face AI. TechRxiv. June 26, 2024.

References

• https://github.com/ultralytics/ultralytics

• https://github.com/deepcam-cn/yolov5-face

• https://github.com/derronqi/yolov7-face