On Designing a SWIR Multi-Wavelength Facial-Based Acquisition System

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).