Аннотация
This paper presents an enhanced algorithm for the generation of color biometric QR codes capable of encoding facial image data, anthropometric parameters, and personal identity information simultaneously within a single RGB-based QR structure. The proposed approach extends existing monochrome QR models by integrating optimized image decomposition, modular QR block generation, and multi-channel RGB encoding to achieve higher data density, improved privacy protection, and better readability under various lighting and compression conditions. The algorithm was implemented in Python using the OpenCV library, ensuring compatibility with contemporary biometric systems, embedded devices, and mobile platforms. Experimental evaluations conducted on standard face databases demonstrate the method’s robustness in terms of decoding accuracy, distortion resilience, and information integrity. Furthermore, the study explores new applications such as animated QR codes and photo–sketch hybrid datasets for training and validation purposes. The results highlight the potential of color biometric QR technology for secure identification, access control, and digital identity verification, offering a novel bridge between computer vision and information security.