Multi-Modal Face Recognition by Means of Augmented Normal Map and PCA

Face represents a rich biometric identifier whose potential in term of discriminating power has not been fully exploited yet. This paper addresses face recognition through a multi-modal approach operating on face's 3D (geometry) and 2D (skin texture) features by means of two different metrics: augmented normal map and principal component analysis. Augmented normal map includes shape (surface normals represented as 24 bit colour pixels) and texture info (additional 8 bit for skin colour) into one 32 bit image. The proposed two-staged method firstly performs a fast one-to-many comparison of facial geometry exploiting normal map metric. Then, to further improve recognition precision and reliability, best rank faces are compared to probe by PCA resulting in a final score. Other advantages are robustness to facial expressions and the ability to selectively filter face's non-skin regions (beard, moustaches). We include preliminary experimental results on a dataset of 101 textured 3D faces.