Facial Recognition Based Criminal Alert System with Blockchain Secured Evidence Storage

Abstract

Facial recognition has become an essential technology in modern surveillance and law enforcement for the automatic identification of individuals from images and video streams. Conventional facial recognition techniques often experience reduced accuracy due to variations in illumination, facial pose, occlusion, low-quality images, and aging effects. To address these challenges, this paper proposes a Blockchain-Based Criminal Recognition and Evidence Management System that integrates advanced deep learning models with secure blockchain technology. The proposed system employs Multi-task Cascaded Convolutional Networks (MTCNN) for accurate face detection and facial alignment, followed by StyleGAN for age progression and age transformation to generate age-invariant facial representations while preserving the individual's identity. The transformed facial images are then processed by a Convolutional Neural Network (CNN)-based facial recognition model to extract discriminative facial features and accurately identify suspects by comparing them with a criminal database. Upon successful recognition, the system automatically generates real-time alerts for authorized personnel and securely stores recognition results, timestamps, confidence scores, and evidence metadata on a blockchain using Web3.py and Ganache, ensuring data integrity, transparency, traceability, and protection against unauthorized modification. By combining robust face detection, ageinvariant facial recognition, and tamper-proof evidence management, the proposed system provides an accurate, secure, and reliable solution for modern criminal identification and digital forensic investigations.