Researchers from the National Research University of Technology MISIS team up with colleagues from the National Research University Higher School of Economics and the AIRI Artificial Intelligence Institute to introduce a system that automatically selects the most effective neural networks for facial recognition services (Face ID). This solution is designed to streamline Face ID operations on smartphones, tablets, and smart devices, bringing faster verification to everyday use. The project has been shared as a breakthrough by MISIS spokespeople.
Face ID services typically involve multiple neural networks responsible for analyzing a range of image parameters. The MISIS system acts as software that speeds up and automates the selection of Face ID components, with the aim of delivering the most reliable recognition results. In determining which Face ID components to deploy, the Russian development takes into account the specific technical characteristics of each device where the service will run. As a result, Face ID systems chosen through this approach can operate within roughly 5 to 10 milliseconds even on lower-powered devices, enabling near-instant verification in many scenarios.
The primary advantage of the MISIS program is its potential to accelerate the development and deployment of facial recognition systems across organizations whose device fleets include hardware with varying levels of computing power. This capability is especially valuable for large-scale deployments where a one-size-fits-all model would struggle to perform consistently across diverse hardware setups.
“Picture a fleet of a million tablets that require a facial recognition module,” explained Andrey Savchenko, deputy director of the MISIS center for artificial intelligence. “Each device has its own microchip and set of capabilities. Our approach unboxes a device and installs an application that determines the most suitable model for that exact tablet and recognizes faces in very short timeframes, such as 5, 10, or 20 milliseconds.”
Key benefits include reduced manual configuration, faster rollout, and improved reliability across a heterogeneous technology landscape. Manually selecting neural network components for Face ID can be both time-consuming and prone to inconsistencies, which may lead to suboptimal performance on many devices. The MISIS solution addresses these challenges by automatically tailoring the recognition pipeline to the capabilities of each endpoint, helping organizations deploy secure and responsive identity services at scale.
The project leverages open source code and is accessible via the GitHub platform. Additional technical details are provided in a peer-reviewed article in IEEE Access, expanding the knowledge base for researchers and engineers in North America and beyond. This worldwide context supports ongoing innovation and interoperability across consumer and enterprise devices that rely on facial recognition technologies.
Experts who have previously explored neuromorphic and AI technologies note that the ongoing evolution of facial recognition tools will hinge on adaptable, device-aware architectures. The MISIS approach demonstrates how modular components can be automatically matched to device capabilities, delivering consistent performance even as hardware varies. This reflects a broader trend toward intelligent, on-device optimization that reduces latency and enhances privacy by limiting data transfer to cloud services when possible. The discussion around these advances continues to stimulate dialogue about the future role of artificial intelligence in everyday digital experiences.