Bibliographic citations
Higa, J., (2024). Desarrollo e implementación de un sistema de control de asistencia de estudiantes basado en reconocimiento facial” [Universidad Nacional de Trujillo]. https://hdl.handle.net/20.500.14414/22671
Higa, J., Desarrollo e implementación de un sistema de control de asistencia de estudiantes basado en reconocimiento facial” []. PE: Universidad Nacional de Trujillo; 2024. https://hdl.handle.net/20.500.14414/22671
@misc{renati/1046032,
title = "Desarrollo e implementación de un sistema de control de asistencia de estudiantes basado en reconocimiento facial”",
author = "Higa Caldeira, Joao Pedro",
publisher = "Universidad Nacional de Trujillo",
year = "2024"
}
The main objective of this engineering project is to develop and implement a student attendance control system based on facial recognition in the school of Mechatronics Engineering. First, we proceeded to determine the requirements of the system, which, in summary, consists of a fast, efficient and robust operation. Then we proceeded to obtain the face database, for which in this case the Labeled Faces in the Wild (LFW) and FEI Database datasets were used, since the former is the most common when training convolutional Siamese networks and the latter due to its similarity with the environment in which the system was implemented. Subsequently, a face recognition architecture was selected, taking into account accuracy, robustness, user capacity, ease of registration and the number of images required for recognition. A total of 4 pre-trained models (ResNet50, Xception, SeReSNext50 and Inception ResNet V2), two architectures (Convolutional Siamese Network and Convolutional Siamese Network with Triple Loss) and two datasets (LFW, FEI) were trained, giving a total of 16 trainings. The best performing model was the convolutional Siamese network with triple loss based on ResNet50 using the LFW dataset, presenting a model accuracy of 71.87%. Then, an integrated application with a graphical user interface (GUI) was implemented, which, using the model obtained, performs the attendance taking, as well as the registration of new students and the export of data in Excel format. Finally, this system obtained an accuracy of 92.5% and an average response time of 3.3 seconds.
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