3D face identification - Experiments towards a large gallery

3D face recognition technologies, with a computation time of a few seconds, perform well for person verification. However, current 3D face recognition approaches are too slow for person identification, even for a watch list of only a few hundred face models. By transforming scanned 3D faces into a canonical face format, storage size is greatly compressed and standard feature extraction is enabled: combining these advantages allows a probe scan to be matched to hundreds or thousands of gallery scans in a few seconds on a commodity computer. We report several experiments that extract a sparse feature representation from the canonical 3D face surface and then perform recognition of a probe face based on the sparse features. We expect to have a trade off between algorithm speed and recognition performance. The best results achieved so far are a rank-1 recognition rate of 98.2% and a speed of 1900 face matches per second. Extrapolating these results suggests that multistage systems could achieve comparable or better recognition rates over large galleries within 5 seconds of compute time.