Future Directions in Deep Learning-Based Facial Attribute Recognition: Integrating Blockchain for Secure and Privacy-Preserving Systems

Abstract

Facial Attribute Recognition (FAR) is an important area of artificial intelligence that uses deep learning techniques to identify facial characteristics such as age, gender, emotions, and identity from images. Recent advances in deep learning have significantly improved the accuracy of these systems. However, the increasing use of FAR applications has raised concerns about data privacy, security, fairness, and transparency. This paper reviews the latest deep learning approaches used in facial attribute recognition, including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and multi-task learning models. It also discusses the major challenges faced by current FAR systems, such as security threats, dependence on centralized data storage, and limited control over user consent and data access. To address these issues, this paper proposes a future framework that combines blockchain technology with facial attribute recognition. The proposed system uses federated learning to train models without sharing sensitive facial data, blockchain to securely record and track data usage, and zero-knowledge proofs to verify user information without exposing personal details. These technologies help improve privacy, security, and trust in FAR systems. The framework is evaluated using well-known datasets such as CelebA, LFW, and IMDB-WIKI. The results show that the proposed approach maintains high recognition accuracy while providing better privacy protection and transparency.