Thesis Details

Hluboké neuronové sítě pro rozpoznání tváří ve videu

Bachelor's Thesis Student: Stratil Jan Academic Year: 2016/2017 Supervisor: Hradiš Michal, Ing., Ph.D.
English title
Deep Learning for Facial Recognition in Video
Language
Czech
Abstract

This bachelor's thesis deals with facial recognition in video using deep neural networks. This task is split into 2 parts. The first part deals with training network that produces compact feature vector which represents the face identity from a video frame. The second part deals with training aggregation network that aggregates those feature vectors into one. This aggregation is fast and it has shown that its results are better than naive pooling methods. Results are tested on the LFW dataset, where it achieves 92.8% accuracy and on the YTF dataset, where the accuracy is 84.06%.

Keywords

Facial recognition, deep neural networks, convolutional neural networks, machine learning, feature vektor, aggregation

Department
Department of Computer Graphics and Multimedia FIT BUT
Degree Programme
Information Technology
Files
Status
defended, grade A
Date
12 June 2017
Reviewer
Sochor Jakub, Ing.
Committee
Herout Adam, prof. Ing., Ph.D. (DCGM FIT BUT), předseda
Češka Milan, doc. RNDr., Ph.D. (DITS FIT BUT), člen
Drábek Vladimír, doc. Ing., CSc. (DCSY FIT BUT), člen
Hliněná Dana, doc. RNDr., Ph.D. (DMAT FEEC BUT), člen
Rychlý Marek, RNDr., Ph.D. (DIFS FIT BUT), člen
Citation
STRATIL, Jan. Hluboké neuronové sítě pro rozpoznání tváří ve videu. Brno, 2017. Bachelor's Thesis. Brno University of Technology, Faculty of Information Technology. 2017-06-12. Supervised by Hradiš Michal. Available from: https://www.fit.vut.cz/study/thesis/19665/
BibTeX 
@bachelorsthesis{FITBT19665,
    author = "Jan Stratil",
    type = "Bachelor's thesis",
    title = "Hlubok\'{e} neuronov\'{e} s\'{i}t\v{e} pro rozpozn\'{a}n\'{i} tv\'{a}\v{r}\'{i} ve videu",
    school = "Brno University of Technology, Faculty of Information Technology",
    year = 2017,
    location = "Brno, CZ",
    language = "czech",
    url = "https://www.fit.vut.cz/study/thesis/19665/"
}

Theses

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