SSL-FER

[BMVC 2022] This is the official code of our Paper "Revisiting Self-Supervised Contrastive Learning for Facial Expression Recognition"

[project] [paper]

Citing

If you find our work useful in your research please consider citing our paper:

@inproceedings{Shu_2022_BMVC,
author    = {Yuxuan Shu and Xiao Gu and Guang-Zhong Yang and Benny P L Lo},
title     = {Revisiting Self-Supervised Contrastive Learning for Facial Expression Recognition},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year      = {2022},
url       = {https://bmvc2022.mpi-inf.mpg.de/0406.pdf}
}

About

The success of most advanced facial expression recognition works relies heavily on large-scale annotated datasets. However, it poses great challenges in acquiring clean and consistent annotations for facial expression datasets. On the other hand, self-supervised contrastive learning has gained great popularity due to its simple yet effective instance discrimination training strategy, which can potentially circumvent the annotation issue. Nevertheless, there remain inherent disadvantages of instance-level discrimination, which are even more challenging when faced with complicated facial representations. In this paper, we revisit the use of self-supervised contrastive learning and explore three core strategies to enforce expression-specific representations and to minimize the interference from other facial attributes, such as identity and face styling. Experimental results show that our proposed method outperforms the current state-of-the-art self-supervised learning methods, in terms of both categorical and dimensional facial expression recognition tasks.

Highlights

We explored three main promising solutions in Self-supervised learning-based Facial Expression Recognition (FER).

·Positives with Same Expression

✔ TimeAug - We added additional temporal shifts to generate positive pairs.

✔ FaceSwap - We swapped between faces to get same expression on different faces with a low computational cost.

·Negatives with Same Identity

✔ HardNeg - We sampled with a larger time-interval to avoid identity-related shortcuts.

·False Negatives Cancellation

✔ MaskFN - We minimized the negative effects caused by false negatives using mouth-eye descriptors.

Dataset

We pretrained on VoxCeleb1 dataset and evaluated on two Facial Expression Recognition datasets, AffectNet and FER2013. We further evaluated on a Face Recognition dataset, LFW. In our experiment, we used the released version provided by scikit-learn. The usage can be found here.

The code for evaluation can be found as follows:

• AffectNet & LFW: Code have been made available here - ClaudiaShu/EMO_AffectNet.
• FER2013: We follow usef-kh/fer for FER2013 evaluation.

Training

Dataset preparation

Please download the Voxceleb dataset through: VoxCeleb

Self-supervised pretraining

With MaskFN

python run_maskclr.py 

With FaceSwap

python run_swapclr.py

Note: We present our work separately in two streams for clarity. Both run_maskclr.py and run_swapclr.py have integrated with temporal sampling on positive augmentation and hard-negative pairs generation.

Please find the code for downstream evaluation here: AffectNet evaluation

Code overview

.
├── data_aug                          
├── dataset   
│   ├── vox1_frames.txt                 # vox1 frame information
├── models 			
│   ├── model_MLP.py                    # MLP layer              
│   └── model_RES.py                    # backbone ResNet model      
├── run_maskclr.py                      # run pretraining with maskFN
├── run_swapclr.py                      # run pretraining with FaceSwap
├── trainer_swapclr.py
└── trainer_swapclr.py

Requirements

To run the script you'll need to install dlib (http://dlib.net) including its Python bindings, and OpenCV.

You'll also need to obtain the trained model from dlib:
http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
http://dlib.net/files/mmod_human_face_detector.dat.bz2

Dependencies

You'll need a working Python environment to run the code. We tested the code based on:

• python 3.8.13
• torch 1.8.1
• torchvision 0.9.1
• CUDA 11.6
• numpy == 1.21.6
• pandas == 1.4.2

Trained Model

Models can be downloaded from here

Results

Pretrained Methods	Datasets	EXPR-F1(↑)	EXPR-Acc(↑)	Valence-CCC(↑)	Valence-RMSE(↓)	Arousal-CCC(↑)	Arousal-RMSE(↓)
BYOL	VoxCeleb1	56.3%	56.4%	0.560	0.460	0.462	0.386
MoCo-v2	VoxCeleb1	56.8%	56.8%	0.570	0.454	0.486	0.378
SimCLR	VoxCeleb1	57.5%	57.7%	0.594	0.431	0.451	0.387
CycleFace	VoxCeleb1,2	48.8%	49.7%	0.534	0.492	0.436	0.383
-	-	-	-	-	-	-	-
TimeAug	VoxCeleb1	57.8%	57.9%	0.583	0.448	0.500	0.374
TimeAug+HardNeg	VoxCeleb1	58.1%	58.3%	0.594	0.437	0.500	0.373
TimeAug+HardNeg+CutMix	VoxCeleb1	58.3%	58.4%	0.542	0.463	0.508	0.368
TimeAug+FaceSwap+MaskFN	VoxCeleb1	58.6%	58.7%	0.568	0.444	0.502	0.369
TimeAug+HardNeg+FaceSwap	VoxCeleb1	58.8%	58.9%	0.601	0.429	0.514	0.367
TimeAug+HardNeg+MaskFN	VoxCeleb1	58.9%	58.9%	0.578	0.448	0.493	0.370
TimeAug+HardNeg+FaceSwap+MaskFN	VoxCeleb1	59.3%	59.3%	0.595	0.435	0.502	0.372

Acknowledgement

• Our code is inspired by: SimCLR
• The evaluation on FER2013 is based on: this repo