Reconocimiento facial en informativos televisivos mediante redes convolucionales profundas
DOI:
https://doi.org/10.17979/ja-cea.2025.46.12046Palabras clave:
Procesamiento de imágenes, Redes neuronales, Aprendizaje máquina, Técnicas de inteligencia artificial, Visión por computadorResumen
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.
Referencias
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Derechos de autor 2025 Ricardo Asensi-González, Pedro Javier Herrera
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
