Facial Expression Recognition with Manifold Learning and Graph Convolutional Network

Olufisayo Ekundayo, Serestina Viriri, Reolyn Heymann

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Facial Expression Recognition (FER) has the ability to detect human affect state. Most of the methods employed for FER task do not really consider the correlation among FER data labels to resolve data annotation and ambiguity problems. Label Distribution Learning (LDL) application to FER considerably address this, but only in the presence of data with distribution labels. Therefore, methods that would recover label distribution from logical labels are required. This work is presenting a graph-based label enhancement approach with manifold learning and Graph Convolutional Network (GCN) for facial expression recognition. The manifold learning approach transforms FER data as a graphical problem, where the data points are considered as nearest neighbours represent graph nodes, with the motive of representing the distances along the edges of the neighbouring graph with the approximate distances along the manifold. This process uses the nearest neighbour graph to learn the geometric structure in FER data, which also learn the possible correlation among the data labels. The graphical convolutional network is employed to incorporate the information provided in the manifold learning and the logical description of the data to classify the nodes of the graph using the information of the nearest neighbours. The experiment conducted on the Binghampton University-3D Facial Expression (BU-3DFE) and the Cohn Kanade extension (CK+) data shows that the model gives promising results.

Original languageEnglish
Title of host publicationPan-African Artificial Intelligence and Smart Systems - Second EAI International Conference, PAAISS 2022, Proceedings
EditorsTelex Magloire Ngatched Nkouatchah, Isaac Woungang, Jules-Raymond Tapamo, Serestina Viriri
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages17
ISBN (Print)9783031252709
Publication statusPublished - 2023
Event2nd International Conference on Pan-African Intelligence and Smart Systems, PAAISS 2022 - Dakar, Senegal
Duration: 2 Nov 20224 Nov 2022

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume459 LNICST
ISSN (Print)1867-8211
ISSN (Electronic)1867-822X


Conference2nd International Conference on Pan-African Intelligence and Smart Systems, PAAISS 2022


  • Facial Expression Recognition
  • Graph Convolutional Network
  • Label distribution learning
  • Label enhancement
  • Manifold learning

ASJC Scopus subject areas

  • Computer Networks and Communications


Dive into the research topics of 'Facial Expression Recognition with Manifold Learning and Graph Convolutional Network'. Together they form a unique fingerprint.

Cite this