An ensemble approach using a frequency-based and stacking classifiers for effective facial expression recognition

Facial Expression Recognition is an essential aspect of human behavior to communicate effectively. A more profound understanding of human behavior, accurate analysis, and interpretation of the emotional content is essential. Hence, facial features play a crucial role as they contain beneficial infor...

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Bibliographic Details
Published inMultimedia tools and applications Vol. 82; no. 10; pp. 14689 - 14712
Main Authors Adyapady R., Rashmi, Annappa, B.
Format Journal Article
LanguageEnglish
Published New York Springer US 01.04.2023
Springer Nature B.V
Subjects
Accuracy
Classification
Classifiers
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Datasets
Deep learning
Emotions
Face recognition
Feature extraction
Human behavior
Machine learning
Multimedia
Multimedia Information Systems
Special Purpose and Application-Based Systems
Stacking
Deep learning
Facial expression recognition
Emotion recognition
Ensemble model
Machine learning
Stacking classifier
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Summary:Facial Expression Recognition is an essential aspect of human behavior to communicate effectively. A more profound understanding of human behavior, accurate analysis, and interpretation of the emotional content is essential. Hence, facial features play a crucial role as they contain beneficial information about facial expressions. A baseline architecture belonging to the EfficientNet family of models is explored for feature extraction. In this work, two novel strategies, the ensemble model using the frequency-based voting approach (FV-EffNet) and the stacking classifier (SC-EffNet), are proposed to enhance classification results’ performance. The proposed system deals with both profile and frontal pose variations. The combination of deep learning models with a stacking classifier gave the best results of 98.35% and 98.06%, and the frequency-based approach used with the ensemble classifier achieved superior performance of 98.71% and 98.56% on Oulu-CASIA and RaFD datasets, respectively. The experiment results with the proposed methodology showed better performance than previous studies on Oulu-CASIA and RaFD datasets, making it more robust to pose variations.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-022-13940-7