In forensic authentication, one aims to identify the perpetrator among a series of suspects or distractors. A
fundamental problem in any recognition system that aims for identification of subjects in a natural scene is
the lack of constrains on viewing and imaging conditions. In forensic applications, identification proves even
more challenging, since most surveillance footage is of abysmal quality. In this context, robust methods for pose
estimation are paramount. In this paper we will therefore present a new pose estimation strategy for very low
quality footage. Our approach uses 3D-2D registration of a textured 3D face model with the surveillance image
to obtain accurate far field pose alignment. Starting from an inaccurate initial estimate, the technique uses
novel similarity measures based on the monogenic signal to guide a pose optimization process. We will illustrate
the descriptive strength of the introduced similarity measures by using them directly as a recognition metric.
Through validation, using both real and synthetic surveillance footage, our pose estimation method is shown to
be accurate, and robust to lighting changes and image degradation.
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