  The potential of the nasal region for expression robust 3D face recognition
is thoroughly investigated by a novel five-step algorithm. First, the nose tip
location is coarsely detected and the face is segmented, aligned and the nasal
region cropped. Then, a very accurate and consistent nasal landmarking
algorithm detects seven keypoints on the nasal region. In the third step, a
feature extraction algorithm based on the surface normals of Gabor-wavelet
filtered depth maps is utilised and, then, a set of spherical patches and
curves are localised over the nasal region to provide the feature descriptors.
The last step applies a genetic algorithm-based feature selector to detect the
most stable patches and curves over different facial expressions. The algorithm
provides the highest reported nasal region-based recognition ranks on the FRGC,
Bosphorus and BU-3DFE datasets. The results are comparable with, and in many
cases better than, many state-of-the-art 3D face recognition algorithms, which
use the whole facial domain. The proposed method does not rely on sophisticated
alignment or denoising steps, is very robust when only one sample per subject
is used in the gallery, and does not require a training step for the
landmarking algorithm. https://github.com/mehryaragha/NoseBiometrics
