Sistema de reconocimiento facial en video basado en Deep Learning utilizando MTCNN y VGG-16 con Transfer Learning para identificación de personajes

Rodrigo Hernandez Moncayo; Alejandra Guadalupe  Bravo García; Andric Javier  Rodríguez Gutiérrez; Antonio  Ocampo Muñoz; Asdrúbal  López Chau

Authors

Rodrigo Hernandez Moncayo 📩 Centro Universitario UAEM Valle de México, México
Alejandra Guadalupe Bravo García Centro Universitario UAEM Valle de México, México https://orcid.org/0009-0006-7188-8440
Andric Javier Rodríguez Gutiérrez Centro Universitario UAEM Valle de México, México
Antonio Ocampo Muñoz Centro Universitario UAEM Valle de México, México
Dr. Asdrúbal López Chau Centro Universitario UAEM Zumpango, México https://orcid.org/0000-0001-5254-0939

Keywords:

Español

Abstract

Automatic face recognition has evolved significantly with the use of deep learning techniques, enabling more robust systems that can withstand variations in lighting, pose, and expression. This paper presents the implementation of a face recognition system for video recordings using the MTCNN face detection algorithm, which is used for its accuracy in identifying and locating faces in uncontrolled environments with varying lighting conditions. After face detection, the VGG-16 model was used as a deep feature extractor, leveraging its architecture to employ transfer learning techniques to learn hierarchical and discriminative representations of the face. The system processes the video frames using the OpenCV library, then uses the MTCNN network for facial identification, and finally assigns identities through a classifier trained, as previously mentioned, with the VGG-16 model. In recent studies, authors report that convolutional neural networks consistently outperform traditional methods due to their ability to automate the extraction of relevant features and handle complex environmental conditions. Preliminary results indicate that the MTCNN–VGG-16 integration forms an effective strategy for facial recognition in real-world scenarios, demonstrating high accuracy and stability under diverse visual conditions. This work provides an accessible and reproducible architecture for potential applications in video analytics, surveillance, and intelligent automation

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