Abstract
In his work, we use a broad set of pixel features of low computational cost—which includes first order gray-level parameters, second order textural features, moment invariant features, multi-scale features, and frequency domain features—for pixel classification based on facial feature localization. A Radial Basis Function Neural Network performs the classification into three regions of interest. Morphological filters and intrinsic geometric properties of the human face are combined into a post-processing heuristic to finish the feature localization. We present the results, which are qualitative and quantitative satisfactory.
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Chrisóstomo, H.B., Maia, J.E.B., de Araujo, T.P. (2014). Pixel Classification and Heuristics for Facial Feature Localization. In: Corchado, E., Lozano, J.A., Quintián, H., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2014. IDEAL 2014. Lecture Notes in Computer Science, vol 8669. Springer, Cham. https://doi.org/10.1007/978-3-319-10840-7_21
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DOI: https://doi.org/10.1007/978-3-319-10840-7_21
Publisher Name: Springer, Cham
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