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Measure face similarity based on deep learning
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Mätning av ansiktslikhet baserad på djupinlärning (Swedish)
Abstract [en]

Measuring face similarity is a task in computer vision that is different from face recognition. It aims to find an embedding in which similar faces have a smaller distance than dissimilar ones. This project investigates two different Siamese networks to explore whether these specific networks outperform face recognition methods on face similarity. The best accuracy is from a Siamese convolution neural network, which is 65.11%. Moreover, the best results in a similarity ranking task are obtained from Siamese geometry-aware metric learning. Besides, this project creates a novel dataset with facial image pairs for face similarity.

Abstract [sv]

Mätning av ansiktslikhet är en uppgift i datorseende som skiljer sig från ansiktsigenkänning. Det syftar till att hitta en inbäddning där liknande ansikten har ett mindre avstånd än olika ansikten. Detta projekt undersöker två olika siamesiska nätverk för att utforska om dessa specifika nätverk överträffar ansiktsigenkänningsmetoder på ansiktslikhet. Den bästa noggrannheten är från ett Siamesiskt faltningsnätverk, vilket är 65,11%. Dessutom erhålls de bästa resultaten i en likhetsrankningsuppgift från Siamesisk geometrimedveten metrisk inlärning. Projektet skapar också ett nytt dataset med ansiktsbildpar för ansiktslikhet.

Place, publisher, year, edition, pages
2019. , p. 53
Series
TRITA-EECS-EX ; 2019:512
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-262675OAI: oai:DiVA.org:kth-262675DiVA, id: diva2:1361888
External cooperation
KSTING AB
Supervisors
Examiners
Available from: 2019-11-07 Created: 2019-10-17 Last updated: 2019-11-07Bibliographically approved

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