Feature Selection in Source Camera Identification
Kai San Choi, Edmund Y. M. Lam, Kenneth K. Y. Wong · 2006
Source camera identification is the process of discerning which camera has been used to capture a particular image. In our previous work, we tackled the problem with a vector of thirty-six features to train and test the classifier. The features include the lens aberration parameters and statistical measurements from pixel intensities. In this paper, we focus on reducing the feature set by stepwise discriminant analysis. Simulation is carried out to evaluate the classifier's performance by using the full feature set, reduced feature sets and randomly selected feature sets. The results show that the reduced feature sets can decrease the processing time while also maintain or even improve the classification accuracy under some circumstances.