An Innovative Mean Approach for Plastic Surgery Face Recognition

Among various biometric person identification systems face recognition is most popular as it doesn-t need the object-s cooperation. The actual advantages of face based identification over other biometrics are uniqueness and acceptance. Popularity of plastic surgery procedures is further increased by affordability and various advanced techniques. Facial plastic surgery can be reconstructive to correct facial feature anomalies or cosmetic to improve the appearance. Both corrective as well as cosmetic surgeries alter the original facial information to a great extent thereby posing a great challenge for face recognition algorithms. It has been observed that many face recognition algorithms fail to recognize faces after plastic surgery, which thus poses a new challenge to automatic face recognition. There are several effective methods invented in recent past but they are effective only under certain conditions like illumination, pose, occlusion, etc. So here we are suggesting an innovative approach to find out a mean method that will provide the most accurate result even after the subject has undergone a plastic surgery with higher accuracy and better response rate. In this method we will find a mean image, which is obtained by applying several popular methods like PCA, LBP, Periocular biometrics and Gabor Filter method to the test image along with Euclidean distance measurement for comparing pre and post-surgery face images.