Statistical Uncorrelated Generalized Image Projection Discriminant Analysis
Jing Yang · Mini-micro Systems · 2004
In this paper, a novel image projection analysis method is developed for image feature extraction. First, with contrast to the Liu's projection analysis method, the proposed one has a desirable property that the projective features vectors are mutual uncorrelated. Second, our method has that the separability of the projected set of the samples is considered from global view when calculating the image optimal set of discriminant vectors,that is the projected set of the samples on the image optimal set of discriminant vectors have the best separability in global sense.Furthermore, the proposed method is directly based on image matrices. That is to say, it needs not to convert the image matrix into high dimensional image vector like the previous linear discriminant methods based on image vectors. So much computational time would be saved if using our method for feature extraction. Finally, the proposed method is tested on ORL face databases. The experimental results indicate that the proposed method outperforms Lius', and a recognition rate of 95% on ORL are achieved with ordinary classifiers. The experimental results also indicate that the proposed method is more powerful than Fisherfaces, and more importantly, its speed for feature extraction is nearly 19.68 times faster.