FEASIBILITY OF FACE RECOGNITION ALGORITHMS ON EDGE

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dc.contributor.authorDuisembayev, Dias
dc.date.accessioned2022-07-05T05:48:12Z
dc.date.available2022-07-05T05:48:12Z
dc.date.issued2022-04
dc.description.abstractThe emergence of IoT and its rapid growth increased significance of edge computing. Edge computing is efficient and necessary when the network bandwidth is low and volume of data that needs to be transmitted is large and could be used when IoT involves data images. It would be more efficient to bring computer vision to edge node rather than sending the image to the server. The main objective of this paper was to analyze main face recognition algorithms and compare their feasibility and accuracy on edge. They are: Eigenfaces, Fisherfaces and Local Binary Patterns algorithm. The algorithms were implemented and executed on Raspberry Pi as it is a device that is used often in IoT and edge computing. The algorithms were implemented using OpenCV library and Python programming language and tested on a custom dataset of 150 images of 10 people. Algorithms’ peformance was analyzed in terms of model training time, recognition time and recognition accuracy. The results showed the feasibility of the algorithms on edge. Fisherfaces algorithm recognized subjects 16 times faster than Eigenfaces and 5 times faster than LBPH algorithm, while providing slightly better recognition accuracy than both. Thus, it was concluded that Fisherfaces face recognition algorithm is the most suitable algorithm in terms of feasibility on Raspberry Pi edge device.en_US
dc.identifier.citationDuisembayev, D. (2022). Feasibility of face recognition algorithms on edge (Unpublished master's thesis). Nazarbayev University, Nur-Sultan, Kazakhstanen_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/6369
dc.language.isoenen_US
dc.publisherNazarbayev University School of Engineering and Digital Sciencesen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectResearch Subject Categories::TECHNOLOGYen_US
dc.subjectInternet of Thingsen_US
dc.subjectInternet of Things servicesen_US
dc.subjectIoTen_US
dc.subjectLPWANen_US
dc.subjectCNNen_US
dc.subjectconvolutional neural networksen_US
dc.subjectType of access: Gated Accessen_US
dc.titleFEASIBILITY OF FACE RECOGNITION ALGORITHMS ON EDGEen_US
dc.typeMaster's thesisen_US
workflow.import.sourcescience

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