A robust feature extraction with optimized DBN-SMO for facial expression recognition

Facial expression is the most common technique is used to convey the expressions of human beings. Due to different ethnicity and age, faces differ from one individual to another so that an automatic facial expression analysis and recognition is a difficult operation. To solve this difficulty, this p...

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Veröffentlicht in:Multimedia tools and applications Jg. 79; H. 29-30; S. 21487 - 21512
Hauptverfasser: Vedantham, Ramachandran, Reddy, Edara Sreenivasa
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.08.2020
Springer Nature B.V
Schlagworte:
Algorithms
Belief networks
Computer Communication Networks
Computer Science
Cost analysis
Data Structures and Information Theory
Discrete cosine transform
Face recognition
Feature extraction
Multimedia Information Systems
Optimization
Principal components analysis
Robustness
Special Purpose and Application-Based Systems
Texture recognition
Feature extraction
FER
DCT
SMO
Deep belief network
PCA
ISSN:1380-7501, 1573-7721
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Zusammenfassung:Facial expression is the most common technique is used to convey the expressions of human beings. Due to different ethnicity and age, faces differ from one individual to another so that an automatic facial expression analysis and recognition is a difficult operation. To solve this difficulty, this paper proposes a robust feature extraction with optimized DBN-SMO for facial expression recognition (FER). Initially, the pre-processing stage is performed then texture descriptors of Local Phase Quantization (LPQ), Weber Local Descriptor (WLD) and Local Binary Pattern (LBP) are used to extract the features. Moreover, Discrete Cosine Transform (DCT) features are extracted to enhance the recognition rate and reduce the computational cost. After that, the Principal component analysis (PCA) is used for dimension reduction. Finally, a deep belief network (DBN) with Spider monkey optimization (SMO) algorithm is used to classify basic expressions for FER. Here, SMO algorithm is used to optimize bias factors and initial connection weights that control the efficiency of the DBN. The proposed work is performed in the MATLAB environment. Experiments performed on Karolinska Directed Emotional Faces (KDEF), Man-Machine Interaction (MMI), Cohn Kanade (CK+), Extended Denver Intensity of Spontaneous Facial Actions (DISFA+) and Oulu-Chinese Academy of Science Institute of Automation (Oulu-CASIA) datasets and it provides a classification accuracy of 97.93%, 95.42%, 97.58%, 95.76%, and 92.38% respectively, this is higher than other current procedures for seven-class emotion.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-020-08901-x