Sistema de Reconocimiento Facial Federado en Conjuntos Abiertos basado en OpenMax

• URL: http://arxiv.org/abs/2508.19312v1
• Date: Tue, 26 Aug 2025 09:27:49 GMT
• Title: Sistema de Reconocimiento Facial Federado en Conjuntos Abiertos basado en OpenMax
• Authors: Ander Galván, Marivi Higuero, Jorge Sasiain, Eduardo Jacob,
• Abstract summary: This paper presents the design, implementation, and evaluation of a facial recognition system within a federated learning framework tailored to open-set scenarios.<n>The proposed approach integrates the OpenMax algorithm into federated learning, leveraging the exchange of mean activation vectors and local distance measures to reliably distinguish between known and unknown subjects.<n> Experimental results validate the effectiveness of the proposed solution, demonstrating its potential for enhancing privacy-aware and robust facial recognition in distributed environments.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Facial recognition powered by Artificial Intelligence has achieved high accuracy in specific scenarios and applications. Nevertheless, it faces significant challenges regarding privacy and identity management, particularly when unknown individuals appear in the operational context. This paper presents the design, implementation, and evaluation of a facial recognition system within a federated learning framework tailored to open-set scenarios. The proposed approach integrates the OpenMax algorithm into federated learning, leveraging the exchange of mean activation vectors and local distance measures to reliably distinguish between known and unknown subjects. Experimental results validate the effectiveness of the proposed solution, demonstrating its potential for enhancing privacy-aware and robust facial recognition in distributed environments. -- El reconocimiento facial impulsado por Inteligencia Artificial ha demostrado una alta precisi\'on en algunos escenarios y aplicaciones. Sin embargo, presenta desaf\'ios relacionados con la privacidad y la identificaci\'on de personas, especialmente considerando que pueden aparecer sujetos desconocidos para el sistema que lo implementa. En este trabajo, se propone el dise\~no, implementaci\'on y evaluaci\'on de un sistema de reconocimiento facial en un escenario de aprendizaje federado, orientado a conjuntos abiertos. Concretamente, se dise\~na una soluci\'on basada en el algoritmo OpenMax para escenarios de aprendizaje federado. La propuesta emplea el intercambio de los vectores de activaci\'on promedio y distancias locales para identificar de manera eficaz tanto personas conocidas como desconocidas. Los experimentos realizados demuestran la implementaci\'on efectiva de la soluci\'on propuesta.

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