Abstrait

Face Recognition of Different Modalities Using SIFT and LBP Features

G. Srividhya, Ms. B. Vijaya Lakshmi M.E (Ph.D)

The objective of the project is to transform the samples in different modality to a common feature space. The discriminant features for the modalities are well aligned so that the comparison between them is Possible. In this paper, we proposedmethod to recognize the heterogeneous face recognition. The prototype subjects (i.e., the training set) have an image in each modality (probe and gallery), and the similarity of an image is measured against the prototype images from the corresponding modality. Initially filtered with three different image filters such as Difference of Gaussians, CSDN (Center Surround Devise Normalization), and Gaussian filter. After this process we have to identify the SIFT, CLBP features. It is an algorithm in computer vision to detect and describe local features in images. The similarity between the two pattern images will identify by the kernel similarity. Finally the identified image will be retrieved from the database