title: 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 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.
authors.forename: Vassilis G
authors.initials: VG
authors.lastname: Kaburlasos
authors.forename: Stelios E
authors.initials: SE
authors.lastname: Papadakis
authors.forename: George A
authors.initials: GA
authors.lastname: Papakostas
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005149:Facial Expression; D017143:Fuzzy Logic; D006801:Humans; D007858:Learning; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D013997:Time Factors