Interest-point Based Face Recognition from Range Images

We present a novel approach to interest-point detection tailored to range images. A range image is represented by two images with blob-like patterns that have easily detectable peaks and can be efficiently extracted using convolution kernels. These kernels were designed to produce repeatable and independent blob-like patterns when convolved with the range image. The interest-points correspond to peaks of the patterns after dropping the unstable ones and performing Non-Maximal Suppression (NMS) on their union. The approach was applied to facial range images from the FRGC V2.0 dataset and about 88% repeatability was achieved. Face recognition was also performed by matching the local range regions around the interest-points. An approach based on three levels of matching combined with RANSAC algorithm was used to increase the correct matches and reduce the false ones. Preliminary recognition results for a database of 466 subjects and 1765 probes were 96.33% identification rate and 90% verification rate at 0.1% False Accept Rate (FAR) for faces under neutral expression.