Face recognition using fuzzy kernel learning vector quantization

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)


In recent years, face recognition is widely used in various aspects as a form of technology advancement. Various studies were conducted to improve the accuracy of face recognition. In this research, Learning Vector Quantization and Fuzzy Kernel Learning Vector Quantization were used as a method of classification. The data used in this research was Labeled Face in The Wild-a (LFW-a). This database has no restrictions such as background, expression, position, and so on. Based on test results using LFW-a database, face recognition using LVQ method has highest accuracy at 89,33% and FKLVQ method has highest accuracy at 89,33% as well.

Original languageEnglish
Article number012068
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 4 Dec 2018
Event2nd Mathematics, Informatics, Science and Education International Conference, MISEIC 2018 - Surabaya, Indonesia
Duration: 21 Jul 2018 → …

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