Abstract:
Face recognition has been an active area of study for both computer vision and image processing communities, not only for biometrics but also for human-computer interaction applications. The purpose of the present work is to evaluate the existing 3D face recognition techniques and seek biologically motivated methods to improve them. We especially look at findings in psychophysics and cognitive science for insights. We propose a biologically motivated computational model, and focus on the earlier stages of the model, whose performance is critical for the later stages. Our emphasis is on automatic localization of facial features. We first propose a strong unsupervised learning algorithm for flexible and automatic training of Gaussian mixture models and use it in a novel feature-based algorithm for facial fiducial point localization. We also propose a novel structural correction algorithm to evaluate the quality of landmarking and to localize fiducial points under adverse conditions. We test the effects of automatic landmarking under rigid and non-rigid registration methods. For the rigid registration approach, we implement the iterative closest point method (ICP). The most important drawback of ICP is the computational cost of registering a test scan to each scan in the gallery. By using an average face model in rigid registration, we show that the computation bottleneck can be eliminated. Following psychophysical arguments on the “other race effect”, we reason that organizing faces into different gender and morphological groups will help us in designing more discriminative classifiers. We test this claim by employing different average face models for dense registration. We propose a shape-based clustering approach that assigns faces into groups with nondescript gender and race. Finally, we propose a regular re-sampling step that increases the speed and the accuracy significantly. These components make up a full 3D face recognition system.
