Noses are hard to conceal and relatively invariant to facial expression. Notwithstanding, their use as a biometric has been largely unexplored. Using photometric stereo images, this paper proposes two new features for nose recognition. The first of these uses Fourier descriptors to capture the ridge shape, from the nasion to the tip, and the second uses geometric ratios. Both features are robustly detected using the curvature of the surface normals to locate landmarks. Recognition results for a database of 40 individuals show that, individually, the new features out-perform an eigenface approach for an image of the nasal region. When combined they have a very respectable recognition rate for methods based on one dimensional features, indicating their potential for use within multi-feature recognition systems.
|Name||IET Seminar Digest|
|Publisher||Institution of Engineering and Technology|
|Conference||3rd International Conference on Imaging for Crime Detection and Prevention, ICDP 2009, December 3, 2009 - December 3, 2009|
|Country/Territory||UK United Kingdom|
|Period||1/12/09 → …|