Recognition performance analysis of subpattern-based principal component analysis for different image partition dimensions and different prerocessing methods Alt örüntüye dayali ana bileşenler analizi yönteminin farkli görüntü bölüt boyutlari ve farkli ön işleme yöntemleri i̇çin tanima performans analizi

Kavuşdu U., Apaydin M., TURHAL Ü. Ç.

2010 7th National Conference on Electrical, Electronics and Computer Engineering, ELECO 2010, Bursa, Türkiye, 2 - 05 Aralık 2010, ss.653-656 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Basıldığı Şehir: Bursa
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.653-656
  • Bilecik Şeyh Edebali Üniversitesi Adresli: Evet

Özet

Principal Component Analysis (PCA), as one of the most used method in face recognition applications,is an analysis method aimed at representation of the multivariate data structural. The PCA method, is a linear transformation which maps the high correlated multivariate data to a new coordinate system where the data is uncorrelated. In this paper as a kind of the traditional PCA method called Subpattern-based PCA method's recognition performance is evaluated under different partition dimensions of face images and for different preprocessing methods. In the experimental studies ORL is used as database.