Face Recognition by Fusion of Local and Global Matching Scores using DS Theory: An Evaluation with Uni-classifier and Multi-classifier Paradigm

TL;DR

This paper introduces a face recognition method that combines local and global SIFT feature matching, fused using Dempster-Shafer theory, demonstrating robustness against occlusion and variability.

Contribution

It proposes a novel fusion approach of local and global face matching scores using Dempster-Shafer theory, enhancing robustness in face recognition tasks.

Findings

Effective recognition with partially occluded faces

Improved accuracy over single matching strategies

Robustness demonstrated on ORL and IITK databases

Abstract

Faces are highly deformable objects which may easily change their appearance over time. Not all face areas are subject to the same variability. Therefore decoupling the information from independent areas of the face is of paramount importance to improve the robustness of any face recognition technique. This paper presents a robust face recognition technique based on the extraction and matching of SIFT features related to independent face areas. Both a global and local (as recognition from parts) matching strategy is proposed. The local strategy is based on matching individual salient facial SIFT features as connected to facial landmarks such as the eyes and the mouth. As for the global matching strategy, all SIFT features are combined together to form a single feature. In order to reduce the identification errors, the Dempster-Shafer decision theory is applied to fuse the two matching techniques. The proposed algorithms are evaluated with the ORL and the IITK face databases. The experimental results demonstrate the effectiveness and potential of the proposed face recognition techniques also in the case of partially occluded faces or with missing information.