Nose tip detection on three-dimensional faces using pose-invariant differential surface features

Three-dimensional (3D) facial data offer the potential to overcome the difficulties caused by the variation of head pose and illumination in 2D face recognition. In 3D face recognition, localisation of nose tip is essential to face normalisation, face registration and pose correction etc. Most of the existing methods of nose tip detection on 3D face deal mainly with frontal or near-frontal poses or are rotation sensitive. Many of them are training-based or model-based. In this study, a novel method of nose tip detection is proposed. Using pose-invariant differential surface features - high-order and low-order curvatures, it can detect nose tip on 3D faces under various poses automatically and accurately. Moreover, it does not require training and does not depend on any particular model. Experimental results on GavabDB verify the robustness and accuracy of the proposed method.