Facial Landmarks Detection on Faulty Datasets with Regression Trees and Principal Component Analysis Parametrization
Tracking landmarks points of the human face is an essential step for the construction of interfaces capable of taking advantage of the communicative potential of facial expressions. Many strategies based on parametric models and regression algorithms with boosting can be applied to this problem. This paper proposes a solution based on the combined use of principal component analysis and regression trees. The main purpose of the presented method is to reduce the sensibility of the system to the presence of missing labels when trained with faulty datasets, by the adoption of corrective heuristics. On such cases, the proposed model achieves performance similar to the reference results, obtained by training on fault free datasets.
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