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Journals / Journal of Naval Sciences and Engineering / 2017 / Cilt: 13 - Sayı: 2

PATTERN RECOGNITION FROM FACE IMAGES

Tolga ENSARİ
Pages
14–20
Publication Date
2017-11-30
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Additionally, NMF-PGD can also be used for different kind of pattern recognition problems.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this article, we use projected gradient descent nonnegative matrix factorization (NMF-PGD) method and make pattern recognition analysis on ORL face data set.
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Key Findings & Data:According to experiments, face recognition accuracy rates have different accuracy values because of the k - lower rank value.
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Conclusion & Impact:Additionally, NMF-PGD can also be used for different kind of pattern recognition problems.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Pattern Recognition · Classification · Face Recognition

Abstract

In this article, we use projected gradient descent nonnegative matrix factorization (NMF-PGD) method and make pattern recognition analysis on ORL face data set. Face recognition is one of the critical issues in our life and some security, daily activities and operations use this well known application area. NMF-PGD is a type of nonnegative matrix factorization (NMF) which defined in the literature. In the study, derived NMF-PGD definition and algorithm has been used in order to classify the ORL face images. We give the experimental results in a table and graph. According to experiments, face recognition accuracy rates have different accuracy values because of the k - lower rank value. We change k-values between 25 and 144 to see the performance of NMF-PGD. At the end, we make some analysis and comments on the recognition rates. Additionally, NMF-PGD can also be used for different kind of pattern recognition problems.

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Cite This Article

ENSARİ, T. (2017). PATTERN RECOGNITION FROM FACE IMAGES. Journal of Naval Sciences and Engineering, Cilt: 13 - Sayı: 2, 14–20. https://ajindex.com/articles/238043

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