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Fuzzy match index for scale-invariant feature transform (SIFT) features with application to face recognition with weak supervision

A fuzzy match index for scale-invariant feature transform (SIFT) features is proposed in this study that cumulatively involves all the test SIFT keypoints in the decision-making process. The new fuzzy SIFT classifier is adapted successfully for robust face recognition from complex backgrounds without using any face cropping tools and using only a single training template. The further incorporation of entropy weights ensures that the facial features have a greater role in the soft decision-making as compared with the background features. The highlights of the authors’ work are: (i) The development of a novel highly efficient fuzzy SIFT descriptor matching tool; (ii) incorporation of feature entropy weights to highlight the contribution of facial features; (iii) application to robust face recognition from uncropped images having diverse backgrounds with a single template for each subject. The authors thus allow for weak supervision of the face recognition experiment and obtain high accuracy for 20 subjects of the CALTECH-256 face database, 133 subjects of the labelled faces for the wild dataset and 994 subjects of the FERET database, with state-of-the-art comparisons indicating the supremacy of the authors’ approach.