Accurately estimating spectral reflectance functions from color camera images is a hot research subject that demonstrates tremendous potential for illuminating engineering and computer vision applications. However, the impact of the illumination spectrum and camera responsivity (system functions) on estimation accuracy has not been systematically studied so far, nor has the impact of a “training” spectral reflectance set. In this study, a dual imaging reflectance optimization system is used based on a neural network and optimal system functions that are respectively trained and optimized using several sample sets. Simulations showed that such optimal systems, trained and optimized with the IES TM-30 spectral reflectance set, can have a substantially higher estimation accuracy compared to “real” systems composed of commercially available projector spectra and camera responsivities and that they are sufficiently robust under small changes in system function peak wavelength and spectral width due to changes in working temperature or with passing time. An analysis of the impact of the specific sample set database adopted for neural net training on estimation accuracy showed that training with the IES set results in good and stable performance, even for other sample sets and different illumination spectra. Training with the spectrally uniform IES spectral reflectance set is therefore advised for general-purpose, high-accuracy reflectance estimation systems. A comparison with a state-of-the-art method shows that the proposed method has a higher color prediction accuracy and a significantly shorter running time for realistic images with high resolution.

Previous studies have shown that the radiant flux that needs to be emitted by an illumination system, can be significantly reduced by optimizing its spectral power distribution to the object reflectance spectra, without inducing perceptible chroma or hue shifts of the illuminated objects. In this paper, the idea is explored to vary the spectral power distribution at different positions in the illuminated scene, in order to tailor the color appearance of objects. For this, a spatially variable, laser diode based illumination system is considered with three primaries and large color gamut. The color rendering performance of the illumination system is quantified via the IES TM-30-2018 method. It is shown that it is possible to reach the maximal color gamut score that is theoretically allowed by the corresponding color fidelity score. This is a unique property of an illumination system with a spatially variable spectral power distribution. The radiant flux requirements of this laser diode based illumination system are theoretically investigated for various color rendering settings, showing reduced power requirements for higher color gamut. The possibility to tune color rendering is also experimentally demonstrated with a set-up that consists of a commercially available laser projector with a hyperspectral camera. By including a feedback optimization algorithm, it is possible to reach the targeted color rendering performance.

A grey pixel detection method, based on the illuminant-invariant descriptor in three logarithmic color channels, was proposed and proved effective for illuminant estimation [25]. In this work, an incremental improvement measure, based on Yang’s work, is proposed by redefining the illuminant-invariant descriptor and the grey pixels. In order to improve the accuracy, appropriate parameters of grey pixels ratio and filter size are statistically analyzed and selected on different data sets. Experimental results show that the improved method is not only effective compared with classical statistics-based methods, but also outperforms many learning-based methods.

Objective . In previous studies, the factors affecting the accuracy of imaging photoplethysmography (iPPG) heart rate (HR) measurement have been focused on the light intensity, facial reflection angle, and motion artifacts. However, the factor of specularly reflected light has not been studied in detail. We explored the effect of specularly reflected light on the accuracy of HR estimation and proposed an estimation method for the direction of specularly radiated light. Approach . To study the HR measurement accuracy influenced by specularly reflected light, we control the component of specularly reflected light by controlling its angle. A total of 100 videos from four different reflected light angles were collected, and 25 subjects participated in the dataset collection. We extracted angles and illuminations for 71 facial regions, fitting sample points through interpolation, and selecting the angle corresponding to the maximum weight in the fitted curve as the estimated reflected angle. Main results . The experimental results show that higher specularly reflected light compromises HR estimation accuracy under the same value of light intensity. Notably, at a 60° angle, the HR accuracy (ACC) increased by 0.7%, while the signal-to-noise ratio and Pearson correlation coefficient increased by 0.8 dB and 0.035, respectively, compared to 0°. The overall root mean squared error, standard deviation, and mean error of our proposed reflected light angle estimation method on the illumination multi-angle incidence (IMAI) dataset are 1.173°, 0.978°, and 0.773°. The average Pearson value is 0.8 in the PURE rotation dataset. In addition, the average ACC of HR measurements in the PURE dataset is improved by 1.73% in our method compared to the state-of-the-art traditional methods. Significance . Our method has great potential for clinical applications, especially in bright light environments such as during surgery, to improve accuracy and monitor blood volume changes in blood vessels.

Region of interest (ROI) selection is a key process for remote heart rate measurement approach. Previous studies have shown that the selected ROI could easily be lost under head rotation circumstances and does not perform well under drastic changes in illumination. To solve the problems, this work proposed a novel adaptive ROI selection method. Firstly, we collected a dataset for modeling the signal-to-noise ratio (SNR) evaluated by illumination and reflection angle. Secondly, we established an SNR estimated model by studying the mathematical correlations between light intensity, reflected angle, and SNR of the regions on the face. Then, we proposed a robust ROI selection method based on SNR estimated model for adaptive ROI selection. Finally, the ROI change de-noising method is proposed to remove the ROI change noise for synthesizing the imaging Photoplethysmography (iPPG) signal and calculating the heart rate. Performances of the proposed method were tested on three datasets, including our collected dataset and two public datasets. The experimental results show that on the MR-NIRP CAR dataset, the percentage of the time that the heart rate (HR) error is less than 6 bpm (PET6) value is higher than 7.3% and 4.6%, respectively, compared with the State-of-the-art (SOTA) methods. The SNR, standard deviation error (SD), mean absolute error (MAE), and root mean squared error (RMSE) metrics are also improved on the Head-rotation and PURE datasets. Therefore, our method is robust and can potentially be applied in various scenarios.