Abstract / truncated to 115 words (read the full 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 ... toggle 10 keywords

3d face recognition – human face recognition – 3d registration – automatic landmarking – mixture models – factor analysis – GOLLUM – iterative closest point – thin-plate splines – average face models

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