Fuzzy match index for scale‐invariant feature transform (SIFT) features with application to face recognition with weak supervision. Issue 11 (1st November 2015)

• Record Type: Journal Article
• Authors: Susan, Seba; Jain, Abhishek; Sharma, Aakash; Verma, Shikhar; Jain, Siddhant
• Is Part Of: IET image processing. Volume 9:Issue 11(2015)
• Page Start: 951
• Page End: 958
• Publication Date: 2015-11-01
• Subjects: face recognition -- transforms -- fuzzy set theory -- entropy -- image matching -- decision making; fuzzy match index -- scale‐invariant feature transform features -- SIFT features -- test SIFT keypoints -- decision‐making process -- fuzzy SIFT classifier -- robust face recognition -- facial features -- soft decision‐making -- fuzzy SIFT descriptor matching tool -- feature entropy weights -- uncropped images -- weak supervision -- Caltech‐256 face database -- labelled faces -- wild dataset -- FERET database; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2014.0670
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.