Granule-Based-Classifier (GbC): A Lattice Computing Scheme Applied on Tree Data Structures

Vassilis G. Kaburlasos, Chris Lytridis, Eleni Vrochidou, Christos Bazinas, George A. Papakostas, Anna Lekova, Omar Bouattane, Mohamed Youssfi and Takashi Hashimoto
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Vassilis G. Kaburlasos: HUMAIN-Lab, International Hellenic University (IHU), 65404 Kavala, Greece
Chris Lytridis: HUMAIN-Lab, International Hellenic University (IHU), 65404 Kavala, Greece
Eleni Vrochidou: HUMAIN-Lab, International Hellenic University (IHU), 65404 Kavala, Greece
Christos Bazinas: HUMAIN-Lab, International Hellenic University (IHU), 65404 Kavala, Greece
George A. Papakostas: HUMAIN-Lab, International Hellenic University (IHU), 65404 Kavala, Greece
Anna Lekova: Institute of Robotics, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
Omar Bouattane: SSDIA Lab, ENSET, University Hassan II of Casablanca, Mohammedia 28000, Morocco
Mohamed Youssfi: SSDIA Lab, ENSET, University Hassan II of Casablanca, Mohammedia 28000, Morocco
Takashi Hashimoto: School of Knowledge Science, Japan Advanced Institute of Science and Technology (JAIST), 1-1 Asahidai, Nomi 923-1292, Ishikawa, Japan

Mathematics, 2021, vol. 9, issue 22, 1-23

Abstract: Social robots keep proliferating. A critical challenge remains their sensible interaction with humans, especially in real world applications. Hence, computing with real world semantics is instrumental. Recently, the Lattice Computing (LC) paradigm has been proposed with a capacity to compute with semantics represented by partial order in a mathematical lattice data domain. In the aforementioned context, this work proposes a parametric LC classifier, namely a Granule-based-Classifier (GbC), applicable in a mathematical lattice (T,?) of tree data structures, each of which represents a human face. A tree data structure here emerges from 68 facial landmarks (points) computed in a data preprocessing step by the OpenFace software. The proposed (tree) representation retains human anonymity during data processing. Extensive computational experiments regarding three different pattern recognition problems, namely (1) head orientation, (2) facial expressions, and (3) human face recognition, demonstrate GbC capacities, including good classification results, and a common human face representation in different pattern recognition problems, as well as data induced granular rules in (T,?) that allow for (a) explainable decision-making, (b) tunable generalization enabled also by formal logic/reasoning techniques, and (c) an inherent capacity for modular data fusion extensions. The potential of the proposed techniques is discussed.

Keywords: Granular Computing; human-robot interaction; machine learning; tree data structures (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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