A new Feature Selection method for face recognition based on general data field
Long Zhao, Shuliang Wang, Yi Lin · 2015
Feature selection is an important step when building a classifier for face recognition. It is difficult to classify the high dimensional and small sample data sets such as face data sets pose. Because the high dimensions increase the risk of over fitting and the small samples decrease the accuracy. A new feature selection method for face recognition based on general data field is proposed in this paper. This method adopts the Sw (potential value within class) and Sb (potential value between different classes) to calculate the information entropy of each feature. The representative features have been selected to structure classifier. Well known feature selection techniques for face data sets are implemented and compared with our present method to show its effectiveness. The experiments show that our algorithm effectively reduces the dimensionality of face data sets and keeps the classifier performance.