Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A Comparative Study of Facial Recognition Techniques: With focus on low computational power
University of Skövde, School of Informatics.
University of Skövde, School of Informatics.
University of Skövde, School of Informatics.
2019 (English)Independent thesis Basic level (degree of Bachelor), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Facial recognition is an increasingly popular security measure in scenarios with low computational power, such as phones and Raspberry Pi’s. There are many facial recognition techniques available. The aim is to compare three such techniques in both performance and time metrics.

An experiment was conducted to compare the facial recognition techniques Convolutional Neural Network (CNN), Eigenface with the classifiers K-Nearest Neighbors (KNN) and support vector machines (SVM) and Fisherface with the classifiers KNN and SVM under the same conditions with a limited version of the LFW dataset. The Python libraries scikit-learn and OpenCV as well as the CNN implementation FaceNet were used.

The results show that the CNN implementation of FaceNet is the best technique in all metrics except for prediction time. FaceNet achieved an F-score of 100% while the OpenCV implementation of Eigenface using SVM scored the worst at 15.5%. The technique with the lowest prediction time was the scikit-learn implementation of Fisherface with SVM.

Place, publisher, year, edition, pages
2019. , p. 39
Keywords [en]
Machine Learning, Facial Recognition, Low Computational Power
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:his:diva-17216OAI: oai:DiVA.org:his-17216DiVA, id: diva2:1327708
Subject / course
Informationsteknologi
Educational program
Computer Science - Specialization in Systems Development
Supervisors
Examiners
Available from: 2019-06-20 Created: 2019-06-19 Last updated: 2019-06-20Bibliographically approved

Open Access in DiVA

A Comparative Study of Facial Recognition Techniques(1070 kB)43 downloads
File information
File name FULLTEXT01.pdfFile size 1070 kBChecksum SHA-512
af335e6703509d60c31b4e87e4a72843fc2bfeb900620c344465e05de991a6056c318854159d6061ec404aa01469db34f200daabfe19cf7ef17d88c19a7a50a5
Type fulltextMimetype application/pdf

By organisation
School of Informatics
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 43 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 131 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf