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This paper investigates the performance of different representations of 3D human nasal region for expression robust recognition. By performing evaluations on the depth and surface normal components of the facial surface, the nasal region is shown to be relatively consistent over various expressions, providing motivation for using the nasal region as a biometric. A new efficient landmarking algorithm that thresholds the local surface normal components is proposed and demonstrated to produce an improved recognition performance for nasal curves from both the depth and surface normal components. The use of the Shape Index for feature extraction is also investigated and shown to produce a good recognition performance.

Using 3D Representations of the Nasal Region for Improved Landmarking and Expression Robust Recognition