Identify the surgically altered face images using granular-PCA approach

Plastic surgery provide a way to enhance the facial appearance. The non-linear variations introduced by the plastic surgery has raised a challenge for face recognition algorithms. In this research we match the face image before and after the plastic surgery. First generate non-disjoint face granules at multiple levels of granularity. The feature extractors are used to extract features from the face granules. The features are then processed by using principal component analysis (PCA) algorithm. Evaluate the weighted distance and match the pre and post surgery images based on weighted distance. The proposed system yield high identification accuracy and take less time for recognition as compared to the existing system.