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There has been a significant improvement in stereo matching with the introduction of adaptive support weights. Existing local methods mainly focus on the computation of support weight which is critical in cost aggregation and usually get excellent results. However, the negative effects of occluded regions are often ignored, which results in the problem of foreground fattening and blurred depth borders. This paper proposes a novel support aggregation strategy by utilizing the occlusion information obtained from left-right consistency check. The weights of invalid points are noticeably reduced at each disparity estimation stage. Experimental results on the Middlebury images show that our method is highly effective in improving the disparities of points around occluded areas and depth discontinuities. According to the Middlebury benchmark, the proposed method achieves the best performance among all the local methods. Moreover, our approach can be easily integrated into nearly all the existing support weights strategies.

Face recognition, a kind of biometric identification, researched in several fields such as computer vision, image processing, and pattern recognition is a natural and direct biometric method. Face Recognition Technology has diverse potential over applications in the fields of information security, law enforcement and surveillance, smart cards, access control and more. Face recognition is one of the diverse techniques used for identifying an individual. Generally the image variations because of the change in face identity are less than the variations among the images of the same face under different illumination and viewing angle. Illumination and pose are the two major challenges, among the several factors that influence face recognition. After pose and illumination, the main factors that affect the face recognition performance are occlusion and expression. So in order to overcome these issues, we proposed an efficient face recognition system based on partial occlusion and expression. The similar blocks in the face image are identified and occlusion can be recovered using the block matching technique. This is combined with expression normalized by calculating the Empherical Mode Decomposition feature. Finally, the face can be recognized by using the PCA. From the implementation result, it is evident that our proposed method based on the PCA technique recognizes the face images effectively.

Occlusion detection is an important problem in 3D computer vision which uses multiple views, such as stereo vision. The presence of occlusion complicates the problem of vergence and the subsequent stereo matching in the generation of 3D data. This paper presents an approach which detects the presence of occlusion concurrently during the vergence process. The main limitation of the approach where the maximum correlation coefficient can be very high even when a significant amount of occlusions is present in the stereo images is shown. This paper presents an adaptive method of adjusting the correlation threshold with respect to the contrast-levels of the image being analyzed to alleviate this limitation. The proposed adaptive threshold method ensures that the sensitivity of detecting mismatches is less dependent upon the contrast-levels of the image being analyzed. The computational advantage of the proposed adaptive threshold method over the fixed threshold method is also presented. Experimental results which show the strengths of the proposed adaptive threshold method over the fixed threshold method on real scenes are given.