Texture based feature extraction using symbol patterns for facial expression recognition

Facial expressions can convey the internal emotions of a person within a certain scenario and play a major role in the social interaction of human beings. In automatic Facial Expression Recognition (FER) systems, the method applied for feature extraction plays a major role in determining the perform...

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Bibliographic Details
Published in:Cognitive neurodynamics 2024-04, Vol.18 (2), p.317-335
Main Authors: Kartheek, Mukku Nisanth, Prasad, Munaga V. N. K., Bhukya, Raju
Format: Article
Language:English
Subjects:
Artificial Intelligence
Biochemistry
Biomedical and Life Sciences
Biomedicine
Classification
Cognitive Psychology
Computer Science
Face recognition
Feature extraction
Methods
Neighborhoods
Neurosciences
Pattern recognition
Pixels
Regions
Research Article
Social factors
Texture recognition
Verbal communication
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Summary:Facial expressions can convey the internal emotions of a person within a certain scenario and play a major role in the social interaction of human beings. In automatic Facial Expression Recognition (FER) systems, the method applied for feature extraction plays a major role in determining the performance of a system. In this regard, by drawing inspiration from the Swastik symbol, three texture based feature descriptors named Symbol Patterns (SP 1 , SP 2 and SP 3 ) have been proposed for facial feature extraction. SP 1 generates one pattern value by comparing eight pixels within a 3 × 3 neighborhood, whereas, SP 2 and SP 3 generates two pattern values each by comparing twelve and sixteen pixels within a 5 × 5 neighborhood respectively. In this work, the proposed Symbol Patterns (SP) have been evaluated with natural, fibonacci, odd, prime, squares and binary weights for determining the optimal recognition accuracy. The proposed SP methods have been tested on MUG, TFEID, CK+, KDEF, FER2013 and FERG datasets and the results from the experimental analysis demonstrated an improvement in the recognition accuracy when compared to the existing FER methods.
ISSN:1871-4080
1871-4099
DOI:10.1007/s11571-022-09824-z