Feature selection is an indispensable preprocessing technique for selecting more relevant features and eradicating the redundant attributes. Finding the more relevant features for the target is an essential activity to improve the predictive accuracy of the learning algorithms because more irrelevant features in the original feature space will cause more classification errors and consume more time for learning. Many methods have been proposed for feature relevance analysis but no work has been done using Bayes Theorem and Self Information. Thus this paper has been initiated to introduce a novel integrated approach for feature weighting using the measures viz., Bayes Theorem and Self Information and picks the high weighted attributes as the more relevant features using Sequential Forward Selection. The main objective of introducing this approach is to enhance the predictive accuracy of the Naive Bayesian Classifier.

Face recognition is very important in computer vision. For human being it is easy to identify human face in any posture but it is not an easy task for systems. Feature extraction is useful technique for recognizing faces through systems. It is used for security, criminal records or identification, verification of person, etc. In face images there are variety of face posture. The question is, How to recognize the face? In this paper, discuss certain approaches for face recognition by using feature extraction along with the issues and the recommendations as to which approach is better and suitable.

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