The use of short wave infrared (SWIR) imaging and illumination technology is at the forefront of system development
for military and law enforcement in both night and daytime operational scenarios<sup>1 2 3 4 </sup>. Along with enabling nighttime
operations, a secondary benefit of SWIR imaging is that it offers the possibility to capture images through tinted
materials, such as tinted architectural or automotive glass and sunglass lenses<sup>5</sup>. The use of SWIR technology introduces
challenges to facial recognition when comparing cross-spectrally from a visible gallery to images captured in the SWIR<sup>6</sup>.
The challenges of SWIR facial recognition are further compounded by the presence of tinted materials in the imaging
path due to varying material types, lighting conditions, and viewing angle.
The paper discusses material and optical characterization efforts undertaken to understand the effects of temperature,
interior and exterior light sources, and viewing angle on the quality of facial images captured through tinted materials.
Temperature vs. spectrum curves are shown for tinted architectural, automotive, and sunglass materials over the range of
-10 to 55C. The results of imaging under various permutations of interior and exterior lighting, along with viewing
angle, are used to evaluate the efficacy of eye detection for cross-spectral facial recognition under these conditions.
Most existing face recognition algorithms require face images with a minimum resolution. Meanwhile, the rapidly
emerging need for near-ground long range surveillance calls for a migration in face recognition from close-up distances
to long distances and accordingly from low and constant resolution to high and adjustable resolution. With limited
optical zoom capability restricted by the system hardware configuration, super-resolution (SR) provides a promising
solution with no additional hardware requirements. In this paper, a brief review of existing SR algorithms is conducted
and their capability of improving face recognition rates (FRR) for long range face images is studied. Algorithms
applicable to real-time scenarios are implemented and their performances in terms of FRR are examined using the IRISLRHM
face database . Our experimental results show that SR followed by appropriate enhancement, such as wavelet
based processing, is able to achieve comparable FRR when equivalent optical zoom is employed.
Iris recognition, the ability to recognize and distinguish individuals by their iris pattern, is the most reliable biometric in terms of recognition and identification performance. However, performance of these systems is affected by poor quality imaging. In this work, we extend previous research efforts on iris quality assessment by analyzing the effect of seven quality factors: defocus blur, motion blur, off-angle, occlusion, specular reflection,
lighting, and pixel-counts on the performance of traditional iris recognition system. We have concluded that defocus blur, motion blur, and off-angle are the factors that affect recognition performance the most. We further designed a fully automated iris image quality evaluation block that operates in two steps. First each factor is estimated individually, then the second step involves fusing the estimated factors by using Dempster-Shafer theory approach to evidential reasoning. The designed block is tested on two datasets, CASIA 1.0 and a dataset collected at WVU. Considerable improvement in recognition performance is demonstrated when removing poor quality images evaluated by our quality metric. The upper bound on processing complexity required to evaluate quality of a single image is O(<i>n</i><sup>2</sup> log <i>n</i>), that of a 2D-Fast Fourier Transform.