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Reconocimiento facial en informativos televisivos mediante redes convolucionales profundas

  • Asensi-González, Ricardo [1] ; Herrera, Pedro Javier [1] Árbol académico
    1. [1] Universidad Nacional de Educación a Distancia

      Universidad Nacional de Educación a Distancia

      Madrid, España

  • Localización: Jornadas de Automática, ISSN-e 3045-4093, Nº. 46, 2025
  • Idioma: español
  • DOI: 10.17979/ja-cea.2025.46.12046
  • Títulos paralelos:
    • Face recognition in television news images using deep convolutional networks
  • Enlaces
  • Resumen
    • español

      Este trabajo propone un sistema de inteligencia artificial basado en redes neuronales profundas que permite la detección y reconocimiento de personas concretas en imágenes extraídas de informativos televisivos. Para ello, se ha creado un conjunto de datos (dataset) que consta de 12800 imágenes, centrado principalmente en figuras políticas de ámbito nacional. El sistema propuesto realiza la detección del individuo en la escena de manera automática utilizando la red YOLOv8 y, posteriormente, realiza su reconocimiento a partir del clasificador que proporcione mayor certidumbre. Para ello, se compararon siete arquitecturas de red neuronal convenientemente adaptadas a esta problemática concreta: VGG-16, VGG-19, InceptionV3, Xception, ResNet-101, MobileNetV2 y DenseNet-169, siendo este último el modelo que obtiene en promedio un mejor desempeño en todas las pruebas realizadas. Los resultados confirman la viabilidad del sistema y permiten sentar las bases para futuras investigaciones.

    • English

      This work proposes an artificial intelligence system based on deep neural networks that enables the detection and recognition of specific persons in images extracted from television news. To this end, a dataset consisting of 12800 images was created, focusing primarily on Spanish political figures. The proposed system automatically detects the individual in the scene using the YOLOv8 network and subsequently recognizes the individual using the classifier that provides the greatest certainty. To this end, seven neural network architectures appropriately adapted to this specific problem were compared: VGG-16, VGG-19, InceptionV3, Xception, ResNet-101, MobileNetV2, and DenseNet-169, with the latter model achieving the best average performance across all tests. The results confirm the viability of the system and lay the groundwork for future research.

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