Multimodal 2D-3D face recognition using local descriptors: pyramidal shape map and structural context

In this study, the authors propose a local descriptor based multimodal approach to improve face recognition performance. Pre-processing is done to smooth, resample, and register data. The resampled three-dimensional (3D) face data are applied to extract novel descriptors including pyramidal shape index, pyramidal curvedness, pyramidal mean, and Gaussian curvatures. Proposed pyramidal shape maps are extracted at each level of the Gaussian pyramid on each point of the 3D data to have 2D matrices as representatives of 3D geometry information. A local descriptor structural context histogram, which represents the structure of the image using scale invariant feature transform, is calculated on pyramidal shape map descriptors and texture image to find matched keypoints in 3D and 2D modality, respectively. Score-level fusion by means of sum rule is employed to get a final matching score. Experimental results on the Face Recognition Grand Challenge (FRGC v2) database illustrate verification rates of 99 and 98.65% at 0.1% false acceptance rate for all versus all and ROC III experiments, respectively. On Bosphorus database, verification rate of 95.8% for neutral versus all experiment has been achieved.