Template aging in 3D and 2D face recognition
Ishan Manjani, Hakkı Doğaner Sümerkan, Patrick J. Flynn, Kevin W. Bowyer · 2016
This is the first work to explore template aging in 3D face recognition. We use a dataset of images representing 16 subjects with 3D and 2D face images, and compare short-term and long-term time-lapse matching accuracy. We find that an ensemble-of-regions approach to 3D face matching has much greater accuracy than whole-face 3D matching, or than a commercial 2D matcher. We observe a drop in accuracies with increased time lapse, most with whole-face 3D matching followed by 2D matching and the 3D ensemble of regions approach. Finally, we determine whether the difference in match quality arising with an increased time lapse is statistically significant.