HETEROGENEOUS FACE RECOGNITION USING KERNEL LDA METHOD

Here we propose the novel method to recognize the heterogeneous face recognition. Initially we remove the noise from the image. To remove the noise present in the image we use median filter. The system involves using a relational feature representation for face images by using kernel similarities between a novel face pattern and a set of prototypes. Initially probe image or test image is normalized, and then it passes for Gaussian filter. Gaussians is a feature enhancement algorithm that involves the blurring of an original image less blurred version of the original. A constant plus a measure of local stimulus contrast. Gaussian filter is windowed filter of linear class by its nature is weighted mean. After this process need to identified the MLBP features. It is an algorithm in computer vision to detect and describe local features in images. For any object in an image, interesting points on the object can be extracted to provide a "feature description" of the object. The similarity between the two pattern images will identify by the kernel similarity. Finally the identified image will be retrieved from the database. The Proposed system introduces the kernel approach with LDA classification method. This has main motivation towards increasing accuracy for HFR (Heterogeneous Face Recognition)..