New Fuzzy LBP Features for Face Recognition
This is an incremental improvement for face recognition systems, targeting researchers in computer vision.
The paper tackles face recognition by proposing new fuzzy LBP features that combine LBP, a fuzzy membership function, and central pixel information to overcome traditional LBP limitations, achieving results evaluated on ORL and Sheffield databases with SVM classifier, K-fold, and ROC curves.
There are many Local texture features each very in way they implement and each of the Algorithm trying improve the performance. An attempt is made in this paper to represent a theoretically very simple and computationally effective approach for face recognition. In our implementation the face image is divided into 3x3 sub-regions from which the features are extracted using the Local Binary Pattern (LBP) over a window, fuzzy membership function and at the central pixel. The LBP features possess the texture discriminative property and their computational cost is very low. By utilising the information from LBP, membership function, and central pixel, the limitations of traditional LBP is eliminated. The bench mark database like ORL and Sheffield Databases are used for the evaluation of proposed features with SVM classifier. For the proposed approach K-fold and ROC curves are obtained and results are compared.