Abstract
Face recognition mainly include face feature extraction and recognition (classification) two parts. The basic idea of local binary pattern (LBP) use texture description operator to build a number of local face description, then describe the local combination form global description. LBP operator works with the 3*3 neighborhood pixels for texture description. After scanning the whole image, we get a LBP response images. Then, we use principal component analysis (PCA) method to dimension reduction for response image, finally using K-Nearest Neighbor classifier to classify the test sample. The experimental results show that LBP-PCA-KNN method gains higher recognition rate than PCA-KNN method’s recognition rate, can be effectively used for face recognition.
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More From: DEStech Transactions on Computer Science and Engineering
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