Majority of the image set based face recognition methods use a generatively learned model for each person that is learned independently by ignoring the other persons in the gallery set. In contrast to these methods, this paper introduces a novel method that searches for discriminative convex models that best fit to an individual's face images but at the same time are as far as possible from the images of other persons in the gallery. We learn discriminative convex models for both affine and convex hulls of image sets. During testing, distances from the query set images to these models are computed efficiently by using simple matrix multiplications, and the query set is assigned to the person in the gallery whose image set is closest to the query images. The proposed method significantly outperforms other methods using generative convex models in terms of both accuracy and testing time, and achieves the state-of-the-art results on four of the five tested datasets. Especially, the accuracy improvement is significant on the challenging PaSC, COX and ESOGU video datasets.

Multispectral image fusion has attracted much attention in the area of computer vision based image processing for remote sensing, industrial automation, surveillance, medical and defense applications. The process carried out in image fusion is combining useful information stated on different channels related to the same scene. Since the proposed image fusion technique greatly improve the performance of image classification, segmentation and edge detection, a new solution is required to combine multispectral images in order to get more informative and good visualized one as well as preserving the important details behind them. By considering this fact, we have introduced a new image fusion approach based on the Common Vector Approach (CVA) concept. By examining the visual results, one can observe that CVA method presents good results as compared with Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Singular Value Decomposition (SVD).