International Journal of Computer Techniques Volume 12 Issue 3 | Performance Analysis of Subspace Methods for Robust Recognition of Facial Expressions Using Holistic Features Extraction Approach
Performance Analysis of Subspace Methods for Robust Recognition of Facial Expressions
International Journal of Computer Techniques – Volume 12 Issue 3, May – June – 2025
ISSN :2394-2231 | Visit Journal
Authors
G.P. Hegde – Professor, Dept. of Information Science and Engg., SDMIT, Ujire, Mangalore
gphegde@sdmit.in
Ashwini B – Assistant Professor, Dept. of Information Science and Engg., SDMIT, Ujire, Mangalore
ashwinib@sdmit.in
Abstract
Improvement of facial expression recognition under various occlusions for different applications is a challenging task. This paper emphasizes fusion of holistic color and texture features of face images, reducing feature vector dimensions using subspace methods like PCA, SVD, ICA, and FFA to improve classification accuracy.
Facial expression recognition under different critical conditions is complex and requires robust solutions that integrate machine learning and subspace methods to enhance accuracy.
Keywords
Facial Expressions, Feature Extraction, Subspace Methods, PCA, SVD, ICA, FFA, SVM Classifier
Conclusion
This work demonstrates the effectiveness of subspace methods in improving facial expression recognition under various occlusion scenarios. Feature fusion techniques significantly enhance recognition rates compared to conventional approaches.
References
- V. Bettadapura, “Face Expression Recognition and Analysis: The State of the Art,” IEEE, 2002.
- C. Shan, S. Gong, P. McOwan, “Facial expression recognition based on Local Binary Patterns,” ELSVIER, 2009.
- W. Zhao, R. Chellappa, A. Krishnaswamy, “Discriminant Analysis of Principal Components for Face Recognition,” IEEE, 1998.
- T. Kanade, J. Cohn, Y. Tian, “Comprehensive database for facial expression analysis,” IEEE, 2000.
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