We deal with the problem of human facial expression recognition from digital images. A digital image is preprocessed for feature extraction using moment descriptors; then, it is represented in the product lattice (F 100,≥) of Intervals’Numbers (INs). Learning as well as generalization are carried out in space (F100,≥) by two different Fuzzy Lattice Reasoning (FLR) classifiers based on an inclusion measure function σ : F100 × F100 → [0, 1].We pursue both a stochastic optimization and a parallel implementation of the proposed techniques. Comparative experimental results on three benchmark data sets demonstrate a superior performance of the proposed FLR classification schemes.


S.E. PapadakisV.G. Kaburlasos, G.A. Papakostas, “Two fuzzy lattice reasoning (FLR) classifiers and their application for human facial expression recognition”, Journal of Multiple-Valued Logic and Soft Computingvol. 22, no. 4-6, pp. 561-579, 2014 (Special Issue on Uncertainty Modeling in Knowledge Engineering and Decision MakingGuest Editors: Cengiz Kahraman and Farouk Yalaoui).

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