Paper Title
Mean Shift Based Face Recognition

In modern times, face recognition has become one of the key aspects of computer vision for the commercial and law enforcement applications. One of the ways to accomplish this is by comparing selected features from the image and a facial database. To achieve this goal we are using Mean shift algorithm, which is a simple iterative procedure that shifts each data point to the average of data points in its neighborhood. For Gaussian kernels, mean shift is a gradient mapping. It often adopts the color histogram in RGB which is sensitive to lighting variations. The proposed technique relies on a segmentation of the area under analysis into a set of color homogenous regions. The proposed system needs to be initialized by feeding it a set of training images of faces. This is used to define the face space which is set of images. In this colored image will be converted to Eigen faces then mean shift algorithm used extract features of face. Haar classifier is used to detect the face from the database.