Gradient-based facial encoding for key generation to encrypt and decrypt multimedia data

TL;DR

This paper presents a facial recognition-based encryption system that generates unique 32-bit keys from facial features using HOG and SVM, enabling secure encryption of various data types with AES.

Contribution

It introduces a novel biometric key generation method from facial features for AES encryption, enhancing security and robustness against template spoofing and replay attacks.

Findings

High entropy and correlation analysis confirm key unpredictability.

Effective encryption and decryption across multiple file formats.

Robustness demonstrated through avalanche effect and security metrics.

Abstract

Security systems relying on passwords are vulnerable to being forgotten, guessed, or breached. Likewise, biometric systems that operate independently are at risk of template spoofing and replay incidents. This paper introduces a biocryptosystem utilizing face recognition techniques to address these issues, allowing for the encryption and decryption of various file types through the Advanced Encryption Standard (AES). The proposed system creates a distinct 32-bit encryption key derived from facial features identified by Histogram of Oriented Gradients (HOG) and categorized using Support Vector Machines (SVM). HOG efficiently identifies edge-aligned facial features, even in dim lighting, ensuring that reliable biometric keys can be generated. This key is then used with AES to encrypt and decrypt a variety of data formats, such as text, audio, and video files. This encryption key, derived from an individual's distinctive facial traits, is exceedingly challenging for adversaries to reproduce or guess. The security and performance of the system have been validated through experiments using several metrics, including correlation analysis, Shannon entropy, normalized Hamming distance, and the avalanche effect on 25 different file types. Potential uses for the proposed system include secure file sharing, online transactions, and data archiving, making it a strong and trustworthy approach to safeguarding sensitive information by integrating the uniqueness of facial biometrics with the established security of AES encryption.