| Summary: | In this thesis, improvement of face recognition performance with the use of images from the visible (VIS) and near-infrared (NIR) spectrum is attempted. Face recog- nition systems can be adversely affected by scenarios which encounter a significant amount of illumination variation across images of the same subject. Cross-spectral face recognition systems using images collected across the VIS and NIR spectrum can counter the ill-effects of illumination variation by standardising both sets of images. A novel preprocessing technique is proposed, which attempts the transformation of faces across both modalities to a feature space with enhanced correlation. Direct matching across the modalities is not possible due to the inherent spectral dif- ferences between NIR and VIS face images. Compared to a VIS light source, NIR radiation has a greater penetrative depth when incident on human skin. This fact, in addition to the greater number of scattering interactions within the skin by rays . from the NIR spectrum can alter the morphology of the human face enough to dis- able a direct match with the corresponding VIS face. Several ways to bridge the gap between NIR-VIS faces have been proposed previously. Mostly of a data-driven ap- proach, these techniques include standardised photometric normalisation techniques and subspace projections. A generative approach driven by a true physical model has not been investigated till now. In this thesis, it is proposed that a large proportion of the scattering interactions present in the NIR spectrum can be accounted for using a model for subsurface scattering. A novel subsurface scattering inversion (SSI) algorithm is developed that implements an inversion approach based on translucent surface rendering by the computer graph- ics field, whereby the reversal of the first order effects of subsurface scattering is attempted. The SSI algorithm is then evaluated against several preprocessing tech- niques, and using various permutations of feature extraction and subspace projection algorithms. The results of this evaluation show an improvement in cross spectral face recognition performance using SSI over existing Retinex-based approaches. The top performing combination of an existing photometric normalisation technique, Sequential Chain, is seen to be the best performing with a Rank 1 recognition rate of 92.5%. In addition, the improvement in performance using non-linear projection models shows an element of non-linearity exists in the relationship between NIR and VIS.
|
|---|
