Vesselness features and the inverse compositional AAM for robust face recognition using thermal IR

TL;DR

This paper introduces a novel thermal IR face recognition method combining vesselness features and an inverse compositional AAM, addressing pose, expression, and thermal emission variability, achieving perfect identification on a large database.

Contribution

It is the first to apply AAM to thermal IR images with a new preprocessing step and a robust anatomical feature-based representation, improving accuracy and robustness.

Findings

Achieved 100% identification accuracy on a large thermal IR face database.

Enhanced thermal image detail to improve AAM convergence.

Robust anatomical feature representation outperforms previous methods.

Abstract

Over the course of the last decade, infrared (IR) and particularly thermal IR imaging based face recognition has emerged as a promising complement to conventional, visible spectrum based approaches which continue to struggle when applied in the real world. While inherently insensitive to visible spectrum illumination changes, IR images introduce specific challenges of their own, most notably sensitivity to factors which affect facial heat emission patterns, e.g. emotional state, ambient temperature, and alcohol intake. In addition, facial expression and pose changes are more difficult to correct in IR images because they are less rich in high frequency detail which is an important cue for fitting any deformable model. We describe a novel method which addresses these challenges. To normalize for pose and facial expression changes we generate a synthetic frontal image of a face in a canonical, neutral facial expression from an image of the face in an arbitrary pose and facial expression. This is achieved by piecewise affine warping which follows active appearance model (AAM) fitting. This is the first publication which explores the use of an AAM on thermal IR images; we propose a pre-processing step which enhances detail in thermal images, making AAM convergence faster and more accurate. To overcome the problem of thermal IR image sensitivity to the pattern of facial temperature emissions we describe a representation based on reliable anatomical features. In contrast to previous approaches, our representation is not binary; rather, our method accounts for the reliability of the extracted features. This makes the proposed representation much more robust both to pose and scale changes. The effectiveness of the proposed approach is demonstrated on the largest public database of thermal IR images of faces on which it achieved 100% identification, significantly outperforming previous methods.