Two fuzzy lattice reasoning (FLR) classifiers and their application for human facial expression recognition – Journal of Multiple-Valued Logic and Soft Computing, vol. 22, no. 4-6

Abstract

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.

Citation

S.E. Papadakis, V.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 Computing, vol. 22, no. 4-6, pp. 561-579, 2014 (Special Issue on Uncertainty Modeling in Knowledge Engineering and Decision Making. Guest Editors: Cengiz Kahraman and Farouk Yalaoui).

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