Volume 13, Number 4

New Local Binary Pattern Feature Extractor with Adaptive Threshold for Face Recognition Applications


Soroosh Parsai and Majid Ahmadi, University of Windsor, Canada


This paper represents a feature extraction method constructed on the local binary pattern (LBP) structure. The proposed method introduces a new adaptive thresholding function to the LBP method replacing the fixed thresholding at zero. The introduced function is a Gaussian Distribution Function (GDF) variation. The proposed technique uses the global and local information of the image and image blocks to perform the adaptation. The adaptive function adds to the on-hand im-age’s features by preserving the information of the amplitude of the pixel difference rather than just considering the sign of the pixel difference in the process of LBP coding. This feature improves the accuracy of the face recognition system by providing additional information. The proposed method demonstrates a higher recognition rate than other presented techniques (%97.75). The proposed method was also tested with different types of noise to demonstrate its effectiveness in the presence of various levels of noise. The Extended Yale B dataset was used for the testing along with Support Vector Machine (SVM) as classifier.


Face Recognition, Pre-processing, CLAHE, Gamma Correction, LBP.