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Face recognition method using embedded data in Principal Component Analysis  

Park Chang-Han (Department of Computer Engineering, Kwangwoon University)
Namkung Jae-Chan (Department of Computer Engineering, Kwangwoon University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.42, no.1, 2005 , pp. 17-23 More about this Journal
Abstract
In this paper, we propose face recognition method using embedded data in super states segmentalized that is specification region exist to face region, hair, forehead, eyes, ears, nose, mouth, and chin. Proposed method defines super states that is specification area in normalized size (92×112), and embedded data that is extract internal factor in super states segmentalized achieve face recognition by PCA algorithm. Proposed method can receive specification data that is less in proposed image's size (92×112) because do orignal image to learn embedded data not to do all loaming. And Showed face recognition rate in image of 92×112 size averagely 99.05%, step 1 99.05%, step 2 98.93%, step 3 98.54%, step 4 97.85%. Therefore, method that is proposed through an experiment showed that the processing speed improves as well as reduce existing face image's information.
Keywords
PCA; Embedded data; feature extraction; face recognition; eigenvalue; eigenvector;
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