Using nasal curves matching for expression robust 3D nose recognition

The development of 3D face recognition algorithms that are robust to variations in expression has been a challenge for researchers over the past decade. One approach to this problem is to utilize the most stable parts on the face surface. The nasal region's relatively constant structure over various expressions makes it attractive for robust recognition and, in this paper, the use of features from the three-dimensional shape of nose is evaluated. After denoising, face cropping and alignment, the nose region is cropped and 16 landmarks robustly detected on its surface. Pairs of landmarks are connected, which results in 75 curves on the nasal surface; these curves form the feature set. The most stable curves over different expressions and occlusions (due to glasses) are selected and used for nose recognition. The Bosphorus dataset is used for feature selection and FRGC v2.0 for nose recognition. Results show a rank-one recognition rate of 82.58% using only two training samples with varying expression for 505 different subjects and 4879 samples, and 90.87% and 81.61% using the FRGC v2.0 dataset Spring2003 folder for training and the Fall2003 and Spring2004 folders for testing, for neutral and varying expressions, respectively. Given the relatively low complexity of the features and matching, the varying expression results are encouraging.