Comparison of Four Demosaicing Methods for Facial Recognition Algorithms

Abstract

Multispectral imaging has become more important in several areas during this decade to overcome the limitations of color imaging. There are several types of multispectral acquisition systems, including single-shot cameras that incorporate Multispectral Filter Arrays (MSFA). MSFA is an extension of the color filter array. Acquisition systems that incorporate spectral filter arrays are very fast, lightweight, and able to acquire moving scenes. But these cameras are manufactured with at best software for filter positioning correction without demosaicing software. Hence there is a need to identify a suitable demosaicing algorithm in terms of image quality, computation time, and decorrelation factor. This paper presents a comparative study of four relevant demosaicing methods in the facial recognition process using images acquired with a single-shot MSFA camera designed in our laboratory. To achieve this goal, the four demosaicing methods named bilinear interpolation, discrete wavelet transform, binary tree, and median vector were adapted to multispectral images acquired using a MSFA camera. Evaluations were first performed using the NIQE performance metric and the correlation coefficient. Then Demosaced images were used to train VGG19 neural network to know which demosacing method better contains relevant features for recognition and better computation time. Results reveal that bilinear interpolation provides the less correlated images and the binary tree gives the best quality images with a NIQE of 8.99 and an accuracy of 100% for face recognition.