The Research Of Face Recognition Based On Sift Algorithm Under Non-ideal Condition

The classical auto face recognition algorithms have good adaptability for human face image in the ideal condition, and its recognition rate is also good. But when dealing with the problem of human face recognition in the complex and non-ideal condition, the recognition rate is low and can not meet certain requirements. SIFT algorithm is based on the local characteristics of the object attributes to describe the object, and it has good robustness for the change of the object shape, translation and rotation. In the classical face recognition algorithm based on SIFT, we need to traverse the database to find all of the face image feature point matching the most number of face images to complete face recognition, which constitutes a closed set of face recognition systems. The computation process of the algorithm is complex, and some thresholds set in the algorithm has a prior, which can satisfy the face recognition in the complex condition is verified.This paper extracts SIFT feature points from the human face image through the database, gathering all SIFT feature points from the same class people in a row vector, which is the set of human face model feature from the same class people. Since the outline of the face having a certain similarity, such as the hair, the forehead has a similarity with the background portion, a relatively smooth gradation level changes, according to the face portion to determine the identity of the object will be relatively small reliability, so the contribution of the extracted feature points from those portions to face classification is relatively small, they need to be removed. After getting rid of the small classification feature points from the each class set of human face model, all the rest images will be trained samples to train the individual classification. Due to the limited sample set of the training database, it is difficult for all the face images are included in the training sample under non-ideal conditions. Designing an overall classification and gathering a large number of non-database object images of human faces under various non-ideal situation, finding the common features of face changing from all kinds people and then, train the overall classifier is an supplement for the individual classifier, meanwhile, constitute an open set face recognition system for determining whether the input human face image belonging to the database. The inputting human face image projected to the every human model feature set of the database to generate the relevant similarity vectors, and then after the adding of the individual classifier and overall classifier, outputs a final result to complete the face recognition. Finally, through the experiment comparison, the face recognition based on the SIFT is proved has better performance for the face recognition than the classical face recognition in the nod-ideal condition.