Abstract
In this paper, we present a novel localization method of facial feature points with generalization ability based on a data-driven semi-supervised learning approach. Even though a powerful facial feature detector can be built using a number of human-annotated training data, the collection process is time-consuming and very often impractical due to the high cost and error-prone process of manual annotations. The proposed method takes advantage of a data-driven semi-supervised learning that optimizes a hybrid detector by interacting with a hierarchical data model to suppress and regularize noisy outliers. The competitive performance comparing to other state-of-the-art technology is also shown using benchmark datasets, Bosprous, BioID.
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Kim, Y.Y., Hong, S.J., Rhee, J.H., Nam, M.Y., Rhee, P.K. (2015). Robust Facial Feature Localization using Data-Driven Semi-supervised Learning Approach. In: Nalpantidis, L., Krüger, V., Eklundh, JO., Gasteratos, A. (eds) Computer Vision Systems. ICVS 2015. Lecture Notes in Computer Science(), vol 9163. Springer, Cham. https://doi.org/10.1007/978-3-319-20904-3_15
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DOI: https://doi.org/10.1007/978-3-319-20904-3_15
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