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Lattice Computing Extension of the FAM Neural Classifier for Human Facial Expression Recognition | IEEE Journals & Magazine | IEEE Xplore

Lattice Computing Extension of the FAM Neural Classifier for Human Facial Expression Recognition


Abstract:

This paper proposes a fundamentally novel extension, namely, flrFAM, of the fuzzy ARTMAP (FAM) neural classifier for incremental real-time learning and generalization bas...Show More

Abstract:

This paper proposes a fundamentally novel extension, namely, flrFAM, of the fuzzy ARTMAP (FAM) neural classifier for incremental real-time learning and generalization based on fuzzy lattice reasoning techniques. FAM is enhanced first by a parameter optimization training (sub)phase, and then by a capacity to process partially ordered (non)numeric data including information granules. The interest here focuses on intervals' numbers (INs) data, where an IN represents a distribution of data samples. We describe the proposed flrFAM classifier as a fuzzy neural network that can induce descriptive as well as flexible (i.e., tunable) decision-making knowledge (rules) from the data. We demonstrate the capacity of the flrFAM classifier for human facial expression recognition on benchmark datasets. The novel feature extraction as well as knowledge-representation is based on orthogonal moments. The reported experimental results compare well with the results by alternative classifiers from the literature. The far-reaching potential of fuzzy lattice reasoning in human-machine interaction applications is discussed.
Published in: IEEE Transactions on Neural Networks and Learning Systems ( Volume: 24, Issue: 10, October 2013)
Page(s): 1526 - 1538
Date of Publication: 29 January 2013

ISSN Information:

PubMed ID: 24808591

References

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