Face Recognition Using Hyperspectral Imaging And Deep Learning

Abstract

Face recognition is still a challenging problem as there is a high possibility that the differences existing within a person or subject can exceed the differences present between different persons. Most of the current research work in the field of biometric face recognition has dealt with (Red-GreenBlue) RGB images. However, Hyper Spectral Imaging (HSI), introduces the spectral dimension for improved discrimination and leads to building a more efficient face recognition system. The spectral dimension adds more intricate details to the image. As a result hyper spectral imaging helps in improving face recognition accuracy. The faces are captured at varying spectral wavelengths of the electromagnetic spectra. Hyper spectral imaging often increases facial discrimination by capturing more biometric measurements and thus revealing information that is not revealed by the commonly used RGB images. The proposed methodology for face recognition using hyper spectral imaging includes performing band selection and band fusion on the hyper spectral face cubes and then classifying them using 3 Dimensional Convolution Neural Networks (3D-CNN). The classification using 3D-CNN gave a promising accuracy of 97.3%.