Abstract
To address one of the important and challenging problems – large-scale contentbased face image retrieval. Given a query face image, content-based face image retrieval tries to find similar face images from a large image database.Large-scale content-based face image retrieval is an enabling technology for many emerging applications. In this method, to utilize automatically detected human attributes that contain semantic cues of the face photos to improve content based face retrieval by constructing semantic codeword’s for efficient large scale face retrieval. By leveraging human attributes in a scalable and systematic framework, it use two orthogonal methods named attributeenhanced sparse coding and attribute embedded inverted indexing to improve the face retrieval in the offline and online stages. Then SVM classifier is used to classify and predict about the image. This will calculate the Euclidean distance of the both the query and the dataset image. To investigate the effectiveness of different attributes and vital factors essential for face retrieval.By further enhancing, the accuracy of image retrieval can be improved.
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