Evaluating the informativity of features in dimensionality reduction methods

Abstract

The ultimate goal of pattern recognition is to discriminate different classes with minimum misclassification rate. The feature vector used in classification should be as short as possible to reduce the algorithm complexity and informative enough to be able to discriminate complicated patterns. In this regard, dimensionality reduction methods are utilized to reduce the raw feature vector length and also to make the features more discriminative. In this paper, a face detection scheme is proposed by using discrete cosine transform (DCT) features in Bayesian discriminating features (BDF) classifier. Low redundancy of DCT features, optimal reconstruction property of Hotelling transform as the dimensionality reduction method, and the minimum error rate of Bayesian classifier, all in all, bring about a high detection rate in the proposed scheme. Various experiments, performed on different databases, certify that using more informative feature vectors results in a higher dimensionality reduction and improves the classifier's detection rate.