Face Recognitions Using Centroid Shift and Neural Network-based Principal Component Analysis

KIPS Journal B (2001 ~ 2012) , Vol. 12B, No.6, pp.715-720, June 2005
10.3745/KIPSTB.2005.12B.6.715, Full Text

Abstract

This paper presents a hybrid recognition method of first moment of face image and principal component analysis(PCA). First moment is applied to reduce the dimension by shifting to the centroid of image, which is to exclude the needless backgrounds in the face recognitions. PCA is implemented by single layer neural network which has a learning rule of Foldiak algorithm. It has been used as an alternative method for numerical PCA. PCA is to derive an orthonormal basis which directly leads to dimensionality reduction, and possibly to feature extraction of face image. The proposed method has been applied to the problems for recognizing the 48 face images(12persons * 4 scenes) of 64*64 pixels. The 3 distances such as city-block, Euclidean, negative angle are used as measures when match the probe images to the nearest gallery images. The experimental results show that the proposed method has a superior recognition performances(speed, rate). The negative angle has been relatively achieved more an accurate similarity than city-block or Euclidean.


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Cite this paper

[KIPS Transactions Style]
Y. H. Cho, "Face Recognitions Using Centroid Shift and Neural Network-based Principal Component Analysis," KIPS Journal B (2001 ~ 2012) , Vol.12B, No.6, pp.715-720, 2005, DOI: 10.3745/KIPSTB.2005.12B.6.715.

[IEEE Style]
Yong Hyun Cho, "Face Recognitions Using Centroid Shift and Neural Network-based Principal Component Analysis," KIPS Journal B (2001 ~ 2012) , vol. 12B, no. 6, pp. 715-720, 2005. DOI: 10.3745/KIPSTB.2005.12B.6.715.

[ACM Style]
Cho, Y. H. 2005. Face Recognitions Using Centroid Shift and Neural Network-based Principal Component Analysis. KIPS Journal B (2001 ~ 2012) , 12B, 6, (2005), 715-720. DOI: 10.3745/KIPSTB.2005.12B.6.715.