Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/122599
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dc.contributor.authorMaj, Michał-
dc.contributor.authorKorzeniak, Jacek-
dc.contributor.authorStokłosa, Józef-
dc.contributor.authorBarwiak, Paweł-
dc.contributor.authorBartnik, Bartłomiej-
dc.contributor.authorMaciura, Łukasz-
dc.date.accessioned2024-05-22T08:00:19Z-
dc.date.available2024-05-22T08:00:19Z-
dc.date.issued2024-
dc.identifier.citationMaj, M., Korzeniak, J., Stokłosa, J., Barwiak, P., Bartnik, B., & Maciura, L. (2024). A real-time autonomous machine learning system for face recognition using pre-trained convolutional neural networks. European Research Studies Journal, 27(s2), 3-13.en_GB
dc.identifier.urihttps://www.um.edu.mt/library/oar/handle/123456789/122599-
dc.description.abstractPURPOSE: This paper aims to present a novel real-time, autonomous machine learning system for face recognition. This system employs pre-trained convolutional neural networks for encoding facial images and applies a Naive Multinomial Bayes model for autonomous learning and real-time classification.en_GB
dc.description.abstractDESIGN/METHODOLOGY/APPROACH: The system leverages a pre-trained ResNet50 model to encode facial images from a camera, while cognitive tracking agents collaborate with machine learning models to monitor the faces of multiple people. A novelty detection algorithm based on a Support Vector Machine (SVM) classifier checks whether a detected face is new or already recognized. The system autonomously starts the learning process if an unrecognized face is identified. Real-time classification of individuals relies on a Naive Multinomial Bayes model, with special agents tracking each face.en_GB
dc.description.abstractFINDINGS: Experiments demonstrated that the system can accurately learn new faces appearing within the camera frame in favorable conditions. The key determinant of successful recognition and learning is the novelty detection algorithm, which, if it fails, may assign multiple identities or group new individuals into existing clusters.en_GB
dc.description.abstractPRACTICAL IMPLICATIONS: This system offers a practical solution for real-time, autonomous face recognition, with potential applications in security, access control, and personalized services. Its ability to quickly learn new faces while maintaining classification accuracy ensures adaptability in dynamic environments.en_GB
dc.description.abstractORIGINALITY/VALUE: The research introduces an innovative approach by combining pretrained neural networks with autonomous learning and a novelty detection algorithm to classify faces in real-time. This hybrid method ensures rapid and accurate face recognition while minimizing the need for extensive training data or prolonged training times.en_GB
dc.language.isoenen_GB
dc.publisherUniversity of Piraeus. International Strategic Management Associationen_GB
dc.rightsinfo:eu-repo/semantics/openAccessen_GB
dc.subjectHuman face recognition (Computer science)en_GB
dc.subjectMachine learningen_GB
dc.subjectNeural networks (Computer science)en_GB
dc.subjectArtificial intelligenceen_GB
dc.subjectDeep learning (Machine learning)en_GB
dc.titleA real-time autonomous machine learning system for face recognition using pre-trained convolutional neural networksen_GB
dc.typearticleen_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.description.reviewedpeer-revieweden_GB
dc.identifier.doi10.35808/ersj/3382-
dc.publication.titleEuropean Research Studies Journalen_GB
Appears in Collections:European Research Studies Journal, Volume 27, Special Issue 1 - Part 1

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