Description

Social networks such as face book, twitter etc… Are widely used in our day to day life. Many of them use human face images for their profile. And also people use celebrity’s faces. Now a day’s human faces are mostly used for man imputations such as searching and mining. Face image retrieval using content based method is an emerging technology in many real world applications. Due to different people having similar faces, problems can be faced while we arrive to retrieve for similar faces. To solve this issue technology such as Retrieval based face annotation use common outline for same categories of image. For example Kid cap can be set as constrain to retrieve children’s, long hair for women’s. Two main challenges should be faced while we overcome the existing system that is the first challenge is we have to efficiently short list the similar facial images. On the other hand we have to effectively exploit the short list of face image and its week labeled information that differs from the original face. Our main goal is to retrieve the similar images from large scale database using content based. In existing content based method used low level features for retrieval and also it cannot detect the human faces automatically. Low level features are just appearance and posing in which we cannot get the exact information whether it is similar human faces. The attributes should be selected effectively as it can provide a crystal clear result from all the faces in large scale database. By incorporating low level and high level attribute we can gain promising result to retrieve similar faces from large scale database. An efficient optimization algorithm is proposed to solve the distance based classification algorithm problem. Moreover, an effective sparse reconstruction scheme is developed to perform the face annotation task. We conduct extensive empirical studies on several web facial image databases to evaluate the proposed based classification algorithm from different aspects.