FEASIBILITY OF FACE RECOGNITION ALGORITHMS ON EDGE
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Date
2022-04
Authors
Duisembayev, Dias
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Engineering and Digital Sciences
Abstract
The 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.
Description
Keywords
Research Subject Categories::TECHNOLOGY, Internet of Things, Internet of Things services, IoT, LPWAN, CNN, convolutional neural networks, Type of access: Gated Access
Citation
Duisembayev, D. (2022). Feasibility of face recognition algorithms on edge (Unpublished master's thesis). Nazarbayev University, Nur-Sultan, Kazakhstan