  In this paper we present a novel method that combines a HK curvature-based
approach for three-dimensional (3D) face detection in different poses (X-axis,
Y-axis and Z-axis). Salient face features, such as the eyes and nose, are
detected through an analysis of the curvature of the entire facial surface. All
the experiments have been performed on the FRAV3D Database. After applying the
proposed algorithm to the 3D facial surface we have obtained considerably good
results i.e. on 752 3D face images our method detected the eye corners for 543
face images, thus giving a 72.20% of eye corners detection and 743 face images
for nose-tip detection thus giving a 98.80% of good nose tip localization
