In law enforcement and security applications, the acquisition of face images is critical in producing key trace evidence for the successful identification of potential threats. However, face recognition (FR) for face images captured using different camera sensors, and under variable illumination conditions, and expressions is very challenging. In this paper, we investigate the advantages and limitations of the heterogeneous problem of matching ultra violet (from 100 <i>nm</i> to 400 <i>nm</i> in wavelength) or UV, face images against their visible (VIS) counterparts, when all face images are captured under controlled conditions. The contributions of our work are three-fold; (i) We used a camera sensor designed with the capability to acquire UV images at short-ranges, and generated a dual-band (VIS and UV) database that is composed of multiple, full frontal, face images of 50 subjects. Two sessions were collected that span over the period of 2 months. (ii) For each dataset, we determined which set of face image pre-processing algorithms are more suitable for face matching, and, finally, (iii) we determined which FR algorithm better matches cross-band face images, resulting in high rank-1 identification rates. Experimental results show that our cross spectral matching (the heterogeneous problem, where gallery and probe sets consist of face images acquired in different spectral bands) algorithms achieve sufficient identification performance. However, we also conclude that the problem under study, is very challenging, and it requires further investigation to address real-world law enforcement or military applications. To the best of our knowledge, this is first time in the open literature the problem of cross-spectral matching of UV against VIS band face images is being investigated.
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.
In harsh environmental conditions characterized by unfavorable lighting and pronounced shadows, human recognition based on Short-Wave Infrared (0.9-1.7 microns) images may be advantageous. SWIR imagery (i) is more
tolerant to low levels of obscurants like fog and smoke; (ii) the active illumination source can be eye-safe and
(iii) the active illumination source is invisible to the human eye making it suitable for surveillance applications.
The key drawback of current SWIR-based acquisition systems is that they lack the capability of real-time simultaneous acquisition of multiple SWIR wavelengths. The contributions of our work are four-fold. First, we
constructed a SWIR multi-wavelength acquisition system (MWAS) that can capture face images at 5 different
wavelengths (1150, 1250, 1350, 1450, 1550 nm) in rapid succession using a 5-filter rotating filter wheel. Each
filter has a band pass of 100 nm and all 5 images are acquired within 260 milliseconds. The acquisition system
utilizes a reflective optical sensor to generate a timing signal corresponding to the filter wheel position that is
used to trigger each camera image acquisition when the appropriate filter is in front of the camera. The timing
signal from the reflective sensor transmits to a display panel to confirm the synchronization of the camera with
the wheel. Second, we performed an empirical optimization on the adjustment of the exposure time of the camera
and speed of the wheel when different light sources (fluorescent, tungsten, both) were used. This improved the
quality of the images acquired. Third, a SWIR spectrometer was used to measure the response from the different
light sources and was used to evaluate which one provides better images as a function of wavelength. Finally, the
selection of the band pass filter, to focus the camera to acquire the good quality SWIR images was done by using
a number of image quality and distortion metrics (e.g. universal quality index and Structural index method).