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Analyzing Face Recognition Using Pca and Comparison between Different Distance

Journal: International Journal of Engineering Sciences & Research Technology (IJESRT) (Vol.2, No. 4)

Publication Date:

Authors : ; ;

Page : 683-686

Keywords : : Face recognition; Eigen Face; Principal Component Analysis; Euclidean Distance; Manhattan Distance.;

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Principal component analysis (PCA) is a widely used technique that is quite efficient and reliable when used for face recognition. Face is the most dominant feature that strongly communicates identity of the person. It proves to be very useful and secure if used for biometric identification. Principal component analysis uses Eigen Face approach for extracting effective features (Eigen vectors) from a given face database and these features corresponds to the dissimilarities among the faces. Every face in the database can be represented as a linear combination of these eigenvectors with appropriate weight assoc presents a methodology for face recognition using Principal Component Analysis algorithm and compares two different distance classifiers i.e. Euclidean distance and Manhattan distance on the basis of recogniti by varying the number of Eigen faces

Last modified: 2014-10-02 22:08:05