Maximized Posteriori Attributes Selection from Facial Salient Landmarks for Face Recognition

TL;DR

This paper introduces a robust face recognition method that uses probabilistic graphs of SIFT features on facial landmarks, employing Dempster-Shafer theory for score fusion, effective even with occlusions.

Contribution

It proposes a novel face recognition approach combining probabilistic graph matching of salient landmarks with Dempster-Shafer score fusion, enhancing accuracy with occluded faces.

Findings

Effective on ORL and IITK databases.

Handles partial occlusions well.

Improves face recognition robustness.

Abstract

This paper presents a robust and dynamic face recognition technique based on the extraction and matching of devised probabilistic graphs drawn on SIFT features related to independent face areas. The face matching strategy is based on matching individual salient facial graph characterized by SIFT features as connected to facial landmarks such as the eyes and the mouth. In order to reduce the face matching errors, the Dempster-Shafer decision theory is applied to fuse the individual matching scores obtained from each pair of salient facial features. The proposed algorithm is evaluated with the ORL and the IITK face databases. The experimental results demonstrate the effectiveness and potential of the proposed face recognition technique also in case of partially occluded faces.