Evaluation of Face Recognition using Principle Component Analysis and Two Dimensional Component Analysis
نویسنده
چکیده
In face recognition feature extraction and classification are the two aspects to be focused. In principle component analysis (PCA) based face recognition technique, the 2D face image matrices must be previously transformed in to one dimensional image vectors. In this paper two dimensional principle component analysis(2DPCA) is used to extract the features. Comparing to conventional principle component analysis, two dimensional principle component analysis is based on 2D matrices rather than 1D vectors. The image matrix is formed directly using original image matrices Recognition rate seems to be higher using two dimensional principle component analysis. The experimental results reveal that the feature extracted using two dimensional principle component analysis is computationally more efficient than principle component analysis. Recall, precision, fmeasure, recognition rate are calculated and the results are analyzed for Oracle Research Laboratory (ORL) database and for the database taken using normal digital camera. This paper includes the comparison of various classification methods and analyze the results.
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