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In this paper, a combination of the Local Binary Pattern method with a semantic pixel set of entropy information is used on back propagation networks for face recognition. This method divides the sample data into NxM zones and calculates the feature value of each zone. In this paper the sample data is divided into 6x9 zones, ie 54 zones with the size of each zone is 10x10 pixels. The LBP method is a uniform pattern of each zone and makes comparisons to zones that have the most number of active pixels. Then from the LBP method the semantic method is used to extract the entropy information from each image. From the feature extraction, there are 107 feature values, 54 of LBP and 53 methods of semantic method. The value of the feature is used as input for classification using back propagation networks. 100 sample data were used for training and 60 different sample data were used for recognition level test. From the test conducted got the recognition rate using a combination of two methods of this extraction feature is 98%.

Explore Semantic Pixel Sets Based Local Pattern With Entropy Information for Face Recognition and Application in Backpropagation