Face Recognition Using PCA Based Eigenfaces

Authors:

Priyanka (Manav Rachna College of Engineering, Faridabad)

Yashwant Prasad Singh (Manav Rachna College of Engineering, Faridabad)

Keywords: Principal component analysis, eigenfaces, training faces

Abstract:

Face plays a major role in conveying identity and is vital object for person identification. This paper aims to analyze the performance of PCA based face recognition technique for designing face recognition system. Principal component analysis (PCA) is the most popular model reduction method due to its uniform mean square convergence. The algorithm involves an Eigenface approach which represents a PCA method in which small set of significant features are used to describe the variation between face images. Recognition is done by comparing the input image with the images in training database through Euclidean distance measurement. Experimental results are provided to show the effectiveness of PCA based recognition with 80.2% recognition rate face images with varying expressions.

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