Biometric Privacy Protection

CVIU Lab, Department of Computer Science and Computer Engineering
University of Arkansas
https://uark-cviu.github.io/
Email: khoaluu@uark.edu

Biometric Privacy Protection

Introduction

Biometric privacy protection has become an increasingly important topic in today's digital world, where the use of biometric data such as facial recognition, fingerprints, and voiceprints is becoming more widespread. Biometric data, unlike other forms of personal data, is unique to an individual and cannot be changed, making it particularly sensitive and valuable for identifying and authenticating people. As the use of biometric data continues to grow, concerns around privacy and security have also increased. The collection and storage of biometric data can pose significant risks, including the potential for identity theft, surveillance, and misuse by third parties. To address these concerns, biometric privacy protection has become a critical area of focus. This involves implementing measures to protect the privacy and security of biometric data, such as restricting access to sensitive information, implementing strong encryption and authentication protocols, and ensuring that data is only used for legitimate purposes. In this project, we aim to explore the various approaches to biometric privacy protection, including the legal frameworks, best practices, and emerging technologies. We will examine the challenges and opportunities for improving biometric privacy protection and investigate the impact of biometric data on individual rights and freedoms. By gaining a deeper understanding of biometric privacy protection, we hope to contribute to the ongoing development of policies and practices that prioritize privacy, security, and ethical considerations, while also enabling the benefits of biometric technology to be realized.




Our Solution

Our CVIU Lab focuses on three major technologies of biometric privacy detection:

1. Open-set Large Scale Biometric Recognition. We aim to build the robust in-the-wild recognition models for biometic data, e.g., face recognition, etc.

2. Biometric Privacy Protection. We aim to build the approach that can identify the vulnerable of deep learning model and protect the biometric data.

3. Face Aging Technology.We aim to build an AI model that can synthesize faces at any age so that it helps to enhance the robustness of the face recognition model.

Our Team

Dr. Khoa Luu Thanh Dat Truong Xuan Bac Nguyen
Assistant Professor Ph.D. Candidate Ph.D. Student
khoaluu@uark.edu tt032@uark.edu xnguyen@uark.edu

Publication

[1] Thanh-Dat Truong, Chi Nhan Duong, Kha Gia Quach, Ngan Le, Tien D Bui, and Khoa Luu (2023). LIAAD: Lightweight Attentive Angular Distillation for Large-scale Age-Invariant Face Recognition. Neurocomputing, 2023.

[2] Thanh-Dat Truong, Chi Nhan Duong, Ngan Le, Marios Savvides, and Khoa Luu (2022). Vec2Face-v2: Unveil Human Faces from their Blackbox Features via Attention-based Network in Face Recognition. arXiv, 2022.

[3] Xuan Bac Nguyen, Duc Toan Bui, Chi Nhan Duong, Tien D Bui, and Khoa Luu (2021). Clusformer: A Transformer based Clustering Approach to Unsupervised Large-scale Face and Visual Landmark Recognition. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

[4] Thanh-Dat Truong, Chi Nhan Duong, Minh-Triet Tran, Ngan Le, and Khoa Luu (2021). Fast Flow Reconstruction via Robust Invertible n×n Convolution. Future Internet, 2021.

[5] Chi Nhan Duong, Thanh-Dat Truong, Khoa Luu, Kha Gia Quach, Hung Bui, and Kaushik Roy (2020). Vec2Face: Unveil Human Faces from their Blackbox Features in Face Recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020.

[6] Chi Nhan Duong, Khoa Luu, Kha Gia Quach, Nghia Nguyen, Eric Patterson, Tien D. Bui, and Ngan Le (2019). Automatic Face Aging in Videos via Deep Reinforcement Learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

Sponsors