3D face recognition using facial curves, sparse random projection and fuzzy similarity measure

In this paper, we propose a fuzzy similarity based classification approach for 3D face recognition. In the feature extraction method, we exploit curve concept to represent the 3D facial data, two types of curves was considered: depth-level and depth-radial curves. As the dimension of the obtained features is high, the problem “curse of dimensionality” appears. To solve this problem, the Random Projection (RP) method was used. The proposed classifier performs Fuzzification operation using triangular membership functions for input data and ordered weighted averaging operators to measure similarity. Experiment was conducted using vrml files from 3D Database considering only one training sample per person. The obtained results are very promising for depth-level and depth-radial curves, besides the recognition rates are higher than 98%.