A novel method for recognizing face with partial occlusion via sparse representation

Nuclear-L1 norm joint regression for face reconstruction and recognition with mixed noise

“…To avoid the two constraints speeding down the convergence, model (20) will be converted into an equivalent problem by using an analogous technique as Section 3.1 min…”

“…For case τ ¼ 1, the linearized technique [45] may be applied to obtain a closed-form solution with regard to x for model (17) or (20), then the auxiliary variate z can be left out. Empirical evidence, nevertheless, show that performance of such an approach is worse than Algorithms 1 or 2.…”

“…Min et al [19] first conducted explicit occlusion analysis and then performed face recognition from the nonoccluded regions. Mi et al [20] first segmented an image into several blocks, where the occluded block is determined by an indicator, then the non-occluded blocks of samples are used as the feature for classification. The common characteristic of these work is to detect the location of occlusion in advance, while they donot consider the spatial information of a face image.…”

Denoising star map data via sparse representation and dictionary learning

“…Sparse representation tends to offer better effect in image denoising, therefore it is an extremely powerful tool for engineering [8,9]. But account for the scale of redundant dictionaries which consists of kinds of cascaded basic functions [10] is large, hindering the application in engineering practice.…”

A linear regression based face recognition method by extending probe images

“…In other words, even we use very simple feature extraction method as downsampling, it is highly possible that the recognition results are equal to the cases that very complicated linear features are used [8,9]. Therefore some studies suggested that for face recognition issue more attention should be paid to design high robust classifier since simple linear features are competent [10][11][12]. Usually, a probe sample is with some variations, which makes the feature vector associated with this probe sample deviates from the correct location in the feature space.…”