Federated Generalized Face Presentation Attack Detection

Shao, Rui; Perera, Pramuditha; Yuen, Pong C.; Patel, Vishal M.

doi:10.1109/tnnls.2022.3172316

Cited by 18 publications

(3 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…FedAffect mainly deals with the training problem of recognition of facial expressions from any private and decentralised data on user devices, which promotes federated learning in facial recognition through better data privacy. [7] shown in Figure 5, fully named "Federated Face Presentation Attack Detection", for detecting face presentation attacks in the modern face recognition pipeline. A face presentation attack detection model with good generalisation can be obtained when it is trained with face images from different input distributions and different types of spoof attacks.…”

Section: Fedaffectmentioning

confidence: 99%

“…On the privacy front, the FedAffect framework addresses the training of facial expression recognition with localised raw data, promoting better data privacy in federated learning for facial recognition [6]. The FedPad framework, on the other hand, ensures a collaborated and trained global model that respects user privacy [7]. Lastly, in detecting face spoof attacks, the FedFSAD framework by Chen et al enhances traditional federated learning with multitask learning and manifold regularisation, showing a marked improvement in the detection of face spoof attacks [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The application of federated learning in face recognition: A systematic investigation of the existing frameworks

2024

ACE

View full text Add to dashboard Cite

This paper presents a thorough examination of the recent progress made in applying federated learning to the field of face recognition. As face recognition technology continues to gain widespread adoption across various sectors, issues related to data privacy and efficiency have taken center stage. In response, federated learning, characterized by its decentralized machine learning approach, has emerged as a promising solution to tackle these pressing concerns. This review categorises the current federated learning frameworks for face recognition into four main purposes: Training Efficiency, Recognition Accuracy, Data Privacy, and Spoof Attack Detection. Each category is explored in-depth, highlighting the principles, structures, applicability, and advantages of the frameworks. The paper also delves into the challenges faced in the integration of federated learning and face recognition, such as high computational overhead, model inconsistency, and data heterogeneity. The review concludes with recommendations for future research directions, emphasising the need for model compression, asynchronous communication strategies, and techniques to address data heterogeneity. The findings underscore the potential and challenges of applying federated learning in face recognition, paving the way for more secure and efficient facial recognition systems.

show abstract

Section: Fedaffectmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The application of federated learning in face recognition: A systematic investigation of the existing frameworks

2024

ACE

View full text Add to dashboard Cite

show abstract

“…Each client, typically a mobile device, contains face images of only its owner. Face Presentation Attack Detection (FedPAD) [37] aims to develop generalized fPAD models while ensuring data privacy. Each data owner trains a local fPAD model, and a server aggregates these models without accessing individual private data.…”

Section: B Federated Learning For Face Recognitionmentioning

confidence: 99%

Maintaining Privacy in Face Recognition Using Federated Learning Method

Woubie,

Solomon,

Attieh

2024

IEEE Access

View full text Add to dashboard Cite

The state-of-the-art face recognition systems are typically trained on a single computer, utilizing extensive image datasets collected from various users. Nevertheless, these datasets often contain sensitive personal information that users may hesitate to disclose. To address potential privacy concerns, we explore the application of federated learning, both with and without secure aggregators, in the context of both supervised and unsupervised face recognition systems. Federated learning facilitates the training of a shared model without necessitating the sharing of individual private data, achieving this by training models on decentralized edge devices housing the data. In our proposed system, each edge device independently trains its own model, which is subsequently transmitted either to a secure aggregator or directly to the central server. To introduce diverse data without the need for data transmission, we employ generative adversarial networks to generate imposter data at the edge. Following this, the secure aggregator or central server combines these individual models to construct a global model, which is then relayed back to the edge devices. Experimental findings based on the CelebA datasets reveal that employing federated learning in both supervised and unsupervised face recognition systems offers dual benefits. Firstly, it safeguards privacy since the original data remains on the edge devices. Secondly, the experimental results demonstrate that the aggregated model yields nearly identical performance compared to the individual models, particularly when the federated model does not utilize a secure aggregator. Hence, our results shed light on the practical challenges associated with privacy-preserving face image training, particularly in terms of the balance between privacy and accuracy.

show abstract

Detecting and Recovering Sequential DeepFake Manipulation

Shao

Liu

2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Federated Generalized Face Presentation Attack Detection

Cited by 18 publications

References 35 publications

The application of federated learning in face recognition: A systematic investigation of the existing frameworks

The application of federated learning in face recognition: A systematic investigation of the existing frameworks

Maintaining Privacy in Face Recognition Using Federated Learning Method

Detecting and Recovering Sequential DeepFake Manipulation

Contact Info

Product

Resources

About