Face recognition of vehicle occupants through windshields in unconstrained environments poses a number of unique challenges ranging from glare, poor illumination, driver pose and motion blur. In this paper, we further develop the hardware and software components of a custom vehicle imaging system to better overcome these challenges. After the build out of a physical prototype system that performs High Dynamic Range (HDR) imaging, we collect a small dataset of through-windshield image captures of known drivers. We then reformulate the classical Mertens-Kautz-Van Reeth HDR fusion algorithm as a pre-initialized neural network, which we name the Mertens Unrolled Network (MU-Net), for the purpose of fine-tuning the HDR output of through-windshield images. Reconstructed faces from this novel HDR method are then evaluated and compared against other traditional and experimental HDR methods in a pre-trained state-of-the-art (SOTA) facial recognition pipeline, verifying the efficacy of our approach.
Raman spectroscopy is a powerful technique for determining the chemical composition of a substance. Our objective
is to determine the chemical composition of an unknown substance given a reference library of Raman spectra. The
unknown spectrum is expressed as a linear combination of the reference library spectra and the non-zero mixing
coefficients represent the presence of individual substances, which are not known. This approach is known as the
supervised learning method. The mixing coefficients are usually estimated using the nonnegative least squares (NNLS)
or nonnegative weighted least squares (NNWLS). This problem is a constrained estimation problem due to the presence
of the nonnegativity constraint. In this paper, we present a swarm based algorithm, the particle swarm optimization
(PSO), to estimate the mixing coefficients and Raman spectra. The PSO is used to determine the mixing coefficients.
PSO efficiently finds an optimum solution. Results are presented for simulated data obtained from the Jennifer Kelly
Raman spectra library. The reference library consists of Raman spectra for nine minerals and the measured spectrum is
simulated by using spectrum/spectra of single/multiple minerals. We compare the root mean square error (RMSE) for
parameter estimation and measurement residual and computational time of the NNWLS and nonnegative weighted PSO
(NNWPSO) algorithms.
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