Face Recognition by Using SURF Features with Block-Based Bag of Feature Models

Face recognition is used to identify humans by their face image. Recently it becomes most common application in information security. Bag of features has been successfully applied in face recognition. In our research we use SURF features and try to improve it by using block-based bag of feature models. In this method we partition the image into multiple blocks and we extract SURF features densely on each block. We compare the performance of the original bag of feature model with Grid/Detector method and bag of block-based feature model.