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Journals / Istanbul University Journal of Electrical and Electronics Engineering / 2002 / Cilt: 2 - Sayı: 2

Extraction of facial features using genetic cellular neural networks

Erdem BİLGİLİ
Ramazan ÇOBAN
Osman N. UÇAN
Pages
601–609
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

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

After the training process CNN templates are applied to various images.

📌 3 Key Takeaways

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Objective & Hypothesis:This paper presents proper CNN templates to locate face region consisting of eyes,eyebrows, nose and mouth in frontal face images.
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Key Findings & Data:At the begining, 72 random genes are selected.
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Conclusion & Impact:After the training process CNN templates are applied to various images.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

This paper presents proper CNN templates to locate face region consisting of eyes,eyebrows, nose and mouth in frontal face images. Training of CNN is done by using genetic algorithms. At the begining, 72 random genes are selected. Each gene includes binary codes of the elements of the CNN templates. CNN templates are designed as symmetric to satisfy the stability. After the training process CNN templates are applied to various images.

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Cite This Article

BİLGİLİ, E., ÇOBAN, R., UÇAN, O. N. (2022). Extraction of facial features using genetic cellular neural networks. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 2 - Sayı: 2, 601–609. https://ajindex.com/articles/543948

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References (1)

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