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Title:Razpoznavanje čustvenih izrazov s pomočjo globokih nevronskih mrež : diplomsko delo
Authors:Štefanič, Gregor (Author)
Potočnik, Božidar (Mentor) More about this mentor... New window
Mlakar, Uroš (Co-mentor)
Files:.pdf UN_Stefanic_Gregor_2019.pdf (1,37 MB)
 
Language:Slovenian
Work type:Bachelor thesis/paper (mb11)
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V diplomskem delu smo se ukvarjali z razpoznavanjem čustvenih izrazov z digitalnih slik obrazov. Razpoznavali smo med sedmimi čustvenimi izrazi, vključno z nevtralnim. Pregledali smo obstoječa dela na področju razpoznavanja čustvenih izrazov, preučili globoke nevronske mreže in pripravili arhitekturo, ki je primerna za razpoznavanje čustvenih izrazov s slik. Uporabili smo arhitekturo z residualno nevronsko mrežo. Našo rešitev smo razvili s pomočjo ogrodja TensorFlow in programskega vmesnika Keras. Implementirali in preizkusili smo jo na mešanih slikah iz podatkovnih baz JAFFE, CK in MMI. Natančnost razpoznavanja čustvenih izrazov na 1017 testnih slikah z našo nevronsko mrežo je bila v povprečju 99,3-odstotna, kar je primerljivo oziroma boljše od sorodnih del.
Keywords:razpoznavanje čustvenih izrazov, globoka nevronska mreža, računalniški vid, residualna nevronska mreža
Year of publishing:2019
Place of performance:Maribor
Publisher:[G. Stefanič]
Number of pages:XII, 36 str.
Source:Maribor
UDC:004.8:004.93(043.2)
COBISS_ID:22908694 Link is opened in a new window
NUK URN:URN:SI:UM:DK:7XGDDS2Q
License:CC BY-NC-ND 4.0
This work is available under this license: Creative Commons Attribution Non-Commercial No Derivatives 4.0 International
Views:69
Downloads:27
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:KTFMB - FERI
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Secondary language

Language:English
Title:Facial expression recognition using deep neural networks
Abstract:In this thesis we dealt with facial expression recognition in digital facial images. We distinguished between seven different facial expressions, including neutral. We reviewed existing works dealing with facial expression recognition, examined deep neural networks, and prepared an architecture suitable for facial expression recognition. Our architecture uses a residual neural network. Our solution was developed using TensorFlow and Keras. We implemented and tested the network on mixed images from databases JAFFE, CK, and MMI. Accuracy of facial expression recognition in 1017 test images with our neural network was 99.3% on average, which is comparable or better than related works.
Keywords:facial expression recognition, deep neural network, computer vision, residual neural network


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