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• MODERN APPROACHES TO FACE RECOGNITION IN LOW-LIGHT CONDITIONS: A REVIEW AND THE CONCEPT OF A HYBRID END-TO-END ARCHITECTURE

  D. А. Morozov , V.V. Gilka , А. S. Kuznetsova

  113-133

  2026-07-07

  Abstract ▼

  The article addresses the problem of reliable face recognition in critical areas such as video surveillance and biometric authentication under low-light conditions. Existing approaches typically separate the tasks of image enhancement and face identification, which leads to error accumulation and loss of informative features. The aim of this work is to overcome this limitation by developing and theoretically substantiating a hybrid end-to-end architecture in which image enhancement and face recognition are solved jointly. The study provides a systematic review of modern methods, including classical algorithms (such as histogram equalization and noise suppression) and advanced deep neural networks (including EnlightenGAN, Zero-DCE, ArcFace, and RetinaFace). The main contribution is the integration of generative and identification modules into a single computational graph. The key result of the study is the demonstration that joint optimization of all processing stages within a unified model, unlike fragmented solutions, fundamentally changes the approach to the problem. Theoretical analysis and comparative evaluation of existing concepts show that the proposed architecture ensures a more efficient gradient flow during training, leading to the formation of higher-quality and noise-robust identity features. It is shown that this approach prevents error accumulation between stages and minimizes information loss. The novelty of the work lies in the holistic, end-to-end view of the face recognition problem under low-light conditions. The practical significance is confirmed by the applicability of the architecture in real systems, where its implementation can potentially improve reliability and processing speed by combining heterogeneous tasks into a single optimizable framework.

  The article addresses the problem of reliable face recognition in critical areas such as video surveillance and biometric authentication under low-light conditions. Existing approaches typically separate the tasks of image enhancement and face identification, which leads to error accumulation and loss of informative features. The aim of this work is to overcome this limitation by developing and theoretically substantiating a hybrid end-to-end architecture in which image enhancement and face recognition are solved jointly. The study provides a systematic review of modern methods, including classical algorithms (such as histogram equalization and noise suppression) and advanced deep neural networks (including EnlightenGAN, Zero-DCE, ArcFace, and RetinaFace). The main contribution is the integration of generative and identification modules into a single computational graph. The key result of the study is the demonstration that joint optimization of all processing stages within a unified model, unlike fragmented solutions, fundamentally changes the approach to the problem. Theoretical analysis and comparative evaluation of existing concepts show that the proposed architecture ensures a more efficient gradient flow during training, leading to the formation of higher-quality and noise-robust identity features. It is shown that this approach prevents error accumulation between stages and minimizes information loss. The novelty of the work lies in the holistic, end-to-end view of the face recognition problem under low-light conditions. The practical significance is confirmed by the applicability of the architecture in real systems, where its implementation can potentially improve reliability and processing speed by combining heterogeneous tasks into a single optimizable framework.

• A METHOD FOR CALCULATING CRYPTOGRAPHIC KEYS FROM A PERSON'S BIOMETRIC DATA BASED ON STABLE TRANSFORMATIONS

  I.V. Kaliberda

  36-52

  2025-11-10

  Abstract ▼

  This article discusses the task of converting a person's biometric data into cryptographic keys that provide a high level of security. Biometric data, although unique, does not have sufficient randomness to create strong cryptographic keys. In addition, key storage issues arise: an attacker can steal the template, and the slightest change in the input data (different lighting, facial expressions) creates a risk of inconsistency, which leads to a high frequency of false rejections. As a solution, a cryptographic key generation method is proposed that combines several key technologies to ensure the efficiency and security of the key creation process. The main stages of the method are described, including obtaining a face image, image processing, image analysis with the extraction of necessary features using a convolutional neural network, image transformation (feature vector) into a binary string, and stable transformations. Sustainable transformations are called upon as techniques that are aimed at protecting biometric data: the use of Reed-Solomon correction codes, the generation of a biometrically dependent key, followed by its distribution into parts according to the classical Shamir scheme, encryption. The advantages of this approach have been theoretically justified in the context of reducing the likelihood of false tolerances and false deviations. The results of experiments based on public datasets are presented. It is shown that compared with classical methods simple sampling and some existing schemes (Bio-Hashing without error correction), the proposed solution provides higher accuracy. The presented method provides significant security advantages, making cryptographic systems more suitable for high-security applications