Mobile app for real-time academic attendance registration based on MobileFaceNet Convolutional neural network

Edison Guaichico, Marco Pusdá-Chulde, MacArthur Ortega-Bustamante, Pedro Granda and Iván García-Santillán

Data and Metadata, 2025, vol. 4, 193

Abstract: The attendance record monitors the student's participation in university academic activities, reflecting the commitment to their professional training. However, traditional systems require moderate time to perform this activity and can be susceptible to fraud and errors. In today's technological landscape, facial recognition has become an effective solution to problems in various fields. Currently, all university professors own smartphones. Considering this advantage, this article proposes to develop a mobile application for the registration of academic attendance using advanced artificial intelligence technologies such as Multitasking Cascade Convolutional Networks (MTCNN) in facial detection, MobileFaceNet in facial feature extraction (facial vector) and the Euclidean distance function in the calculation of similarity between obtained vectors. MobileFaceNet was evaluated in Python, using a personalized dataset of top-level students of the Software career of the Universidad Técnica del Norte, achieving an accuracy of 98.9% and 99.4% in LWF. The models were then integrated into a mobile app developed with Android Studio. Finally, the time required to register attendance was compared using the university academic platform (SIIU) and the facial recognition mobile application. The benchmarking showed a 24-second reduction of 33% in attendance registration time.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:4:y:2025:i::p:193:id:1056294dm2025193

DOI: 10.56294/dm2025193

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