Evaluating the effect of the eigenvalues on BDF classifier in face detection

Principal component analysis (PCA) is an effective tool for dimension reduction in classification approaches. Bayesian discriminating features (BDF) is a classifier which effectively utilizes this tool. In this classifier, any of the M largest eigenvalues of the training patterns' covariance matrix are individually involved in classification while the arithmetic average of the remaining eigenvalues take part just as a single parameter. In this paper, by suggesting a new classifier, effect of the number of involved eigenvalues in classification performance is studied. In the suggested classifier we ignore the arithmetic average that is utilized in BDF. Our experiments verify that increasing M does not lead to an ongoing increase in classifier's detection rate in both BDF and the proposed one. However, by over-increasing M, the dependency of classifiers' parameters to the training samples increases which could reduce the performance of the classifiers when they come to make decision about new samples. Furthermore, experimental results verify that arithmetic average of the remaining eigenvalues in BDF improves the classifier performance only when an appropriate number of eigenvalues is selected; hence, ignoring the arithmetic average, as done in proposed classifier, could provide a better performance rather than BDF.