Reconocimiento de emociones faciales mediante técnicas de aprendizaje profundo aplicado a procesos administrativos de la Universidad Técnica de Manabí
Facial emotion recognition using deep learning techniques applied to administrative processes at the Technical University of Manabí
DOI:
https://doi.org/10.53591/iti.v17i24.2665Palabras clave:
Aprendizaje profundo, MobileNetV1, procesos administrativos, reconocimiento de emociones faciales, redes neuronales convolucionalesResumen
Contexto: Las instituciones de educación superior promueven el diseño de soluciones automatizadas para optimizar procesos y servicios; en este estudio se diseñó un prototipo de reconocimiento de emociones faciales aplicado a procesos administrativos de la Universidad Técnica de Manabí (UTM). Método: Se utilizaron datos propios en una red neuronal convolucional (CNN) personalizada, optimizada para imágenes en escala de grises de 48×48 píxeles, y se comparó con la arquitectura preentrenada MobileNetV1. El entrenamiento siguió un enfoque reproducible mediante un flujo de preprocesamiento, aumento y validación de seis clases de emociones (felicidad, enojo, miedo, neutral, tristeza y disgusto). Resultados: La CNN personalizada alcanzó el mejor rendimiento con una exactitud de 0,88 frente a 0,80 de MobileNetV1. El mejor modelo fue integrado en un prototipo con pruebas en escenarios de matrícula, donde la emoción neutral fue la de mayor frecuencia y confianza. Conclusiones: En este contexto, las redes neuronales convolucionales son ratificadas para el reconocimiento de imágenes con enfoque en la gestión de la atención a los usuarios en servicios públicos. En trabajos futuros se pueden mejorar la validación estratificada, la comparación con conjuntos de datos de referencia, así como el establecimiento de criterios éticos para fortalecer la generalización y el uso responsable de datos sensibles.
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