An Adaptive Method of PCA for Minimization of Classification Error Using Naïve Bayes Classifier

This paper focuses on comparative study of calculation of classification error with classical PCA technique and our adaptive method. Frontal face image database with uniform lightening condition and color images with different orientations have taken in order to get better accuracy in the result. Conventional PCA algorithm is applied for dimensional reduction and calculating the classification error. The result generated from this technique is being compared with the result generated from our adaptive method Naïve Bayes Classifier. We have used Naïve Bayes Classifier for calculating the classification error of each feature vector instead of considering K largest Eigen-value as in PCA. A covariance matrix is arranged by considering the feature vector having the lowest K minimum error. We have applied the adaptive technique on 626 colored facial images with uniform illumination conditions and varying poses and 545 frontal facial images with uniform background to get the better accuracy.