How to Boost Face Recognition with StyleGAN? (2210.10090v2)

Abstract: State-of-the-art face recognition systems require vast amounts of labeled training data. Given the priority of privacy in face recognition applications, the data is limited to celebrity web crawls, which have issues such as limited numbers of identities. On the other hand, self-supervised revolution in the industry motivates research on the adaptation of related techniques to facial recognition. One of the most popular practical tricks is to augment the dataset by the samples drawn from generative models while preserving the identity. We show that a simple approach based on fine-tuning pSp encoder for StyleGAN allows us to improve upon the state-of-the-art facial recognition and performs better compared to training on synthetic face identities. We also collect large-scale unlabeled datasets with controllable ethnic constitution -- AfricanFaceSet-5M (5 million images of different people) and AsianFaceSet-3M (3 million images of different people) -- and we show that pretraining on each of them improves recognition of the respective ethnicities (as well as others), while combining all unlabeled datasets results in the biggest performance increase. Our self-supervised strategy is the most useful with limited amounts of labeled training data, which can be beneficial for more tailored face recognition tasks and when facing privacy concerns. Evaluation is based on a standard RFW dataset and a new large-scale RB-WebFace benchmark. The code and data are made publicly available at https://github.com/seva100/stylegan-for-facerec.

Enhancing Face Recognition through StyleGAN Pretraining

The paper entitled "How to Boost Face Recognition with StyleGAN?" investigates an innovative approach for improving face recognition systems, particularly focusing on addressing the challenges posed by limited labeled training data and the necessity for ethnic diversity in datasets. In particular, the authors present a method that utilizes a self-supervised learning approach by leveraging StyleGAN, a state-of-the-art generative model, to pretrain face recognition models.

The paper addresses a fundamental challenge in face recognition: the scarcity of labeled training data due to privacy concerns and the prevalent use of celebrity images. The authors argue that existing datasets are not only limited in size but also lack demographic balance, which is essential for fair and effective face recognition systems. In response, the paper introduces a self-supervised pretraining technique that leverages unlabeled face data via StyleGAN to enhance the subsequent face recognition training.

Methodology

The proposed method involves three key steps:

1. Training StyleGAN2-ADA: The first step involves fitting a StyleGAN2-ADA generator to the distribution of face images in a large, unlabeled dataset. This generator captures the diverse facial characteristics present in the data, which can later be used to inform the face recognition task.
2. Training the pSp Encoder: The second step employs a pixel2style2pixel (pSp) encoder to map real images to latent codes in the learned latent space of StyleGAN. This encoder is crucial for extracting meaningful facial features without relying on identity labels.
3. Fine-tuning for Face Recognition: Finally, the pretrained encoder's weights are transferred to a standard face recognition network, which is then fine-tuned using labeled face data. The authors employ ArcFace loss, among others, to optimize the network for face recognition.

The crux of the methodology lies in the use of diverse, unlabeled datasets during the pretraining phase. The authors introduce two large-scale datasets, AfricanFaceSet-5M and AsianFaceSet-3M, to ensure a rich representation of ethnic diversity, thereby mitigating the biases that can arise in face recognition systems.

Results and Evaluation

The paper presents robust evaluation metrics based on the RFW dataset and a newly developed large-scale benchmark, RB-WebFace. The proposed pretraining strategy is shown to achieve notable performance improvements over baseline models and other state-of-the-art methods, particularly for ethnic groups that are traditionally underrepresented in standard datasets.

The results indicate significant gains in face verification accuracy, especially in scenarios with limited labeled data. The approach demonstrates a 10% improvement in verification accuracy with only 1% of the labeled data, highlighting the efficiency of self-supervised pretraining. Furthermore, the strategic use of demographic-specific data collections allows for tailored improvements in recognition performance across different ethnic groups.

Implications and Future Directions

The research provides a compelling case for integrating generative models and self-supervised learning into face recognition pipelines. Methodologically, it opens up avenues for using unlabeled data at scale, thereby circumventing privacy and bias concerns associated with traditional datasets. The findings suggest that similar approaches could be extended to other domains within computer vision and beyond.

Looking forward, the paper posits several interesting research directions. Combining the training phases into a unified framework could potentially address any information loss in transferring weights. Additionally, experimenting with various backbone architectures or scaling the approach using generative models like transformers may further enhance the system's robustness and adaptability.

In conclusion, this research presents a methodologically rigorous and empirically validated approach to advancing face recognition technologies by leveraging the power of StyleGAN and self-supervised learning, setting a precedent for future explorations in similar domains.