Improvement of a facial recognition system based on one shot camera

Abstract

In recent years, one-shot cameras that integrate Multispectral Filter Arrays (MSFA) are used to acquire multispectral images. In a previous paper, we have proposed a multispectral image recognition system based on this type of camera. The images acquired with these cameras are then demosaiced. Multispectral facial images acquired with our MSFA one-shot camera present information redundancy which leads to a strong correlation between bands. A dimensionality reduction is necessary to reduce information redundancy. Dimensionality reduction is a set of techniques that allow to project an initial image of dimension n into a final image of dimension p, while preserving its relevant information. This paper proposes an improvement of facial recognition system using the Multispectral Filter Array one shot camera. A dimensionality reduction module has been added to the system. A comparison of the performance of different dimensionality reduction methods based on the eigenvalues, and VGG19 classification results are conducted. Experimental results on the EXIST database made up with our camera indicate a good decorrelation of the bands leading to the reduction of bands from eight to three with the Karhuen-Love transform and an accuracy of 100% with VGG19. The application of the dimension reduction method resulted in a 15 % gain in processing time always reaching an accuracy of 100%.