Noise-Removal Markers to Improve PCA-Based Face Recognition

In this paper an approach based on insertion of "markers" is proposed to increase the performance of face recognition based on principal component analysis (PCA). The markers represent zero-valued pixels which are expected to remove information likely to affect classification (noisy pixels). The patterns of the markers was optimized with a genetic algorithm (GA) in contrast to other noise generation techniques. Experiments performed with a well known face database showed that the technique was able to achieve significant improvements on PCA particularly when data for training was small in comparison with the size of testing sets. This was also observed when the number of eigenfaces used for classification was small.