An Improved Method Based on SIFT Feature for Face Recognition
Zhe Lin · Journal of Shantou University · 2013
By aiming at the problem of misrecognition caused by factors such as light,expression and noise,an improved method based on SIFT feature for face recognition is proposed.First of all,a set of SIFT feature vectors are extracted from every training image.A weak classifier can be constructed with a SIFT feature vector and a threshold.For every training image,some weak classifiers are selected with thresholds and weights by an algorithm based on Adaboost.A similarity function for training images is established.Similarity between object image and training images can be calculated by similarity functions.The average similarity between object image and each class is gained.Finally,an object image is classified to the class with the maximum average similarity.It’s verified that this method is able to increase the recognition rate to 96.82% on AR face database and to 98% on ORL face database,which is better than other existing methods.