Abstract
Frontal face images are segmented into 7 regions using only sum and difference histograms as pixel information, without any a priori knowledge. In the training phase, a decision tree is created using a projection pursuit algorithm: in each step, the optimal one-dimensional projection is chosen by a simulated annealing process according to a projection index, and classes are isolated by a decision boundary that maximizes class separability, until the end nodes contain only one class each. Satisfactory qualitative and quantitative results were obtained and presented.
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Laboreiro, V.R.S., Maia, J.E.B., de Araujo, T.P. (2012). Face Segmentation Using Projection Pursuit for Texture Classification. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_29
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DOI: https://doi.org/10.1007/978-3-642-32639-4_29
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