Non Binary Local Gradient Contours for Face Recognition
This addresses face recognition for computer vision applications, but it appears incremental as it builds on existing pattern methods with a theoretical extension.
The authors tackled the problem of ineffective face recognition features from traditional methods like LBP and LDP by proposing a new approach based on information sets to eliminate information loss during binarization, resulting in features that work fairly well compared to eigenface, fisherface, and LBP methods.
As the features from the traditional Local Binary Patterns (LBP) and Local Directional Patterns (LDP) are found to be ineffective for face recognition, we have proposed a new approach derived on the basis of Information sets whereby the loss of information that occurs during the binarization is eliminated. The information sets expand the scope of fuzzy sets by connecting the attribute and the corresponding membership function value as a product. Since face is having smooth texture in a limited area, the extracted features must be highly discernible. To limit the number of features, we consider only the non overlapping windows. By the application of the information set theory we can reduce the number of feature of an image. The derived features are shown to work fairly well over eigenface, fisherface and LBP methods.