PCA ve ICA algoritmaları tabanlı yüz tanıma sisteminin performans karşılaştırması

Abstract

Yüz tanıma sistemi, verimliliği ve doğruluğu nedeniyle biyometrik çalışmalarda baskın bir araştırma alanı haline gelmiştir. Bu teknoloji, insanların otomatik olarak tanımlanması için çeşitli güvenlik uygulamalarına geniş ölçüde yatırım edilmiştir.Bununla birlikte, insanın karmaşıklığı, özellikleri ve görünümlerindeki büyük varyasyonla temsille karşı karşıyadır. Bu karmaşıklık, bu tür sorunları daha az yanlış sonuçlarla etkili bir şekilde öğrenebilen ve üstesinden gelebilen güçlü algoritmaları benimsemeyi içerir. Bu amaçla Temel Bileşen Analizi (PCA) ve Bağımsız Bileşen Analizi (ICA) vb. Bu çalışma, tanıma yöntemleri olarak PCA ve ICA kullanılarak güvenilir bir yüz tanıma sisteminin uygulanmasına odaklanır ve yüz sınıflandırıcı olarak Öklid Mesafesi (ED). PCA ve ICA'nın performansları üzerine bir karşılaştırma yapmıştır. Bu iki yöntem esas olarak bu araştırmada görüntü projeksiyonu ve boyutsallık azaltma için kullanılmaktadır. Sınıflandırma işlemi, ED sınıflandırıcısı tarafından benimsenen mesafe ölçü düzeni kullanılarak gerçekleştirilir. Karşılaştırma, belirli bir yüz görüntüsü kümesini tanıma açısından sistem sağlamlığı değerlendirmesi için alınır.

Keywords

• Biyometri
• Yüz tanıma sistemi
• Kullanıcı doğrulama
• PCA ve ICA algoritmaları

Performance comparison of PCA and ICA algorithms-based face recognition system

Abstract

Face recognition system has become a dominant research area in biometric studies due to its efficiency and accuracy. This technology has been broadly invested in various security applications for the automatic identification of humans. However, the complexity of human faces representation with the large variation in its characteristics and appearances. This complexity involves adopting powerful algorithms that can effectively learn and overcome such problems with less false results. Many algorithms are proposed for this purpose such as the Principal Component Analysis (PCA) and the Independent Components Analysis (ICA), etc. This work focuses on the implementation of a reliable face recognition system using PCA and ICA as recognition methods and the Euclidean Distance (ED) as a face classifier. A comparison is conducted upon the performances of the PCA and the ICA. These two methods are mainly used in this research for image projection and dimensionality reduction. The classification process is performed by using the distance measure scheme that is adopted by the ED classifier. The comparison is taken for the system robustness evaluation in terms of recognizing a given set of face images.

Keywords

• Biometrics
• Face Recognition System
• User Authentication
• PCA and ICA algorithms

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