3D constrained local model with independent component analysis and non-Gaussian shape prior distribution: Application to 3D facial landmark detection

We present a novel statistical shape model and fitting process for the 3D Constrained Local Models (CLM), exploiting the properties of Independent Component Analysis (ICA), instead of the classic use of Principal Component Analysis (PCA), and adopting a non-Gaussian distribution of the shape prior information. Using ICA permits to exploit the real distribution of shape priors by adopting a Generalised Gaussian Distribution (GGD) model. Consequently, we derive a modified approach of the mean shift optimizer by using the Expectation-Maximization algorithms. We apply this novel method for the localization of face landmarks on 3D facial mesh models, which, to the best of our knowledge, is the first employment of the CLM variant on this kind of modality. Experiments conduced on the Bosphorus face database demonstrated that our approach outperforms state-of-the-art methods.