Identificación facial automática en secuencias de vídeo empleando redes neuronales y aprendizaje profundo

En una cadena de televisión se manejan grandes volúmenes de material audiovisual que deben ser organizados y catalogados para integrarse en el archivo histórico y poder reutilizarse en el futuro, aportando así valor añadido. Tradicionalmente, esta tarea recaía en el departamento de documentación, que la realizaba de forma manual. Disponer de un sistema automático capaz de detectar, reconocer y catalogar a las personas que aparecen en las secuencias de vídeo agilizaría el trabajo documental. Este trabajo presenta un sistema de inteligencia artificial basado en redes neuronales profundas capaz de detectar y reconocer personas específicas en las imágenes de informativos televisivos. Para su desarrollo, se elaboró un conjunto de datos compuesto de 18476 imágenes, centrado principalmente en figuras políticas de ámbito nacional. El sistema identifica automáticamente a los individuos en la escena mediante la red YOLOv8-seg y, posteriormente, lleva a cabo su reconocimiento utilizando el clasificador que ofrece mayor fiabilidad. Con este fin, se evaluaron siete arquitecturas de redes neuronales adaptadas a esta tarea, siendo DenseNet-169 la que mostró el mejor rendimiento promedio en las pruebas realizadas. Los resultados obtenidos confirman la viabilidad del sistema y abren el camino para futuras investigaciones.

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