Feature Patch Illumination Spaces and Karcher Compression for Face Recognition via Grassmannians

Jen-Mei Chang, Chris Peterson, Michael Kirby

Department of Mathematics, Colorado State University, Fort Collins, USA.
Departmentof Mathematics and Statistics, California State University, Long Beach, USA.

DOI: 10.4236/apm.2012.24033                

Abstract

Recent work has established that digital images of a human face, when collected with a fixed pose but under a variety of illumination conditions, possess discriminatory information that can be used in classification. In this paper we perform classification on Grassmannians to demonstrate that sufficient discriminatory information persists in feature patch (e.g., nose or eye patch) illumination spaces. We further employ the use of Karcher mean on the Grassmannians to demonstrate that this compressed representation can accelerate computations with relatively minor sacrifice on performance. The combination of these two ideas introduces a novel perspective in performing face recognition.

Keywords

Grassmannians; Karcher Mean; Face Recognition; Illumination Spaces; Compressions; Feature Patches; Principal Angles

Conflicts of Interest

The authors declare no conflicts of interest.

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