In the modern digital age, securing sensitive information during communication is a growing concern owing to the rise in cyber threats. Steganography offers a more discrete solution by hiding messages in digital media. However, many existing methods lack an additional security layer, making them susceptible to unauthorized access. To overcome this challenge, EnigmaID introduces a hybrid framework combining facial recognition with steganography and cryptographic hashing, offering a dual-layer security model not addressed in previous works. Compared to conventional steganographic systems and biometric-only frameworks, EnigmaID achieves higher tamper resistance, enhanced privacy, and adaptability through its integration of adaptive region selection, deep learning auto-encoders, and error-correcting code mechanisms. By integrating deep-learning-based facial authentication, EnigmaID enhances both privacy and accessibility while maintaining the quality of the original image. In contrast, traditional steganographic techniques often compromise image fidelity or lack biometric integration, while biometric-only systems fall short in concealing supplementary identity information securely within document images. © 2025 Elsevier B.V., All rights reserved.