In this paper we compare different computational strategies for skin detection. They differ in the type of data used in the training phase, the type of pre-processing done on the query image, and the level of visual information used. In particular, we define a high-level computational strategy, which uses a face detector in the pre-processing step. Two different implementations of it are proposed: one relies on an adaptive single gaussian model, the other a fixed threshold skin cluster detector on an illuminant-independent image representation. The experimental results on a heterogeneous dataset containing images acquired under uncontrolled lighting conditions show that the high-level strategies outperform low-level ones. © 2013 Springer-Verlag.
CITATION STYLE
Bianco, S., Gasparini, F., & Schettini, R. (2013). Computational strategies for skin detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7786 LNCS, pp. 199–211). https://doi.org/10.1007/978-3-642-36700-7_16
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