Facial recognition using deep neural networks.
|Facial recognition using deep neural networks.
|Translated title :
|[fr] Détection et reconnaissance automatique de visage à l'aide de réseaux neuronaux
|Date of defense :
Van Droogenbroeck, Marc
|Committee's member(s) :
Van Lishout, François
|Number of pages :
|Engineering, computing & technology > Computer science
|Université de Liège, Liège, Belgique
|Master en ingénieur civil en informatique, à finalité spécialisée en "intelligent systems"
|Master thesis of the Faculté des Sciences appliquées
[en] Innovation or the introduction of the new. Innovation has always been the motor of civilization
by introducing new ideas to solve the countless problems that such a complex system encounters. It has spurred economic growth and brought the comfort we live in today. In my opinion, in Belgium, particularly in the south, we have often rested on our laurels and forgot to innovate. This observation fostered my choice to undertake this thesis in the context of the RAGI project. This innovative project, relying on academic research to produce a commercial product, is developing an "intelligent system for recognition, welcoming, and guidance". Innovation in
this project comes partly from the use of a machine learning technique called automatic face
recognition to be able to identify people and guide them in a building. The goal of this thesis
is to study this concept to help the team working on RAGI take appropriate decisions. To
achieve this goal, I search for and analyze state-of-the-algorithms in both face detection and
face recognition. The research for algorithms goes through the analysis of recent benchmarks,
two of which (i.e. WIDER FACE and MegaFace) are also used for evaluating those
algorithms. Simultaneously, this work points out the difficulties to perform such a research
and testing process. The results on these benchmarks allows to determine which algorithms
perform better, that is to say SSH for detection and both Dlib-R and ArcFace for recognition. For detection, the influence of facial attributes such as pose, size or blur is explored. Finally, to have more relatable results with regards to the RAGI project, we designed a specifi c dataset on which the same algorithms are tested. Composed of 494 frames with 3561 annotated faces from 13 different identities, it allowed us to study other parameters while confi rming the results obtained on the publicly available datasets. All those tests are performed with algorithm efficiency in mind and computation time measurements show that the best techniques tend to work slower but that they can achieve practical execution times.
Cite this master thesis
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