International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Methods for Facial Expression Recognition with Applications in Challenging Situations |
|---|---|
| ABSTRACT | In this paper, te fascinating and challenging topic of facial expression analysis has far-reaching implications for areas such as data-driven animation and human-computer interaction, among others. In order to achieve facial expression recognition, it is essential to develop an efficient representation of the face using the source photos. Local Binary Patterns (LBP), a method for statistically representing faces, is evaluated in a real-world setting. On different datasets, several machine learning algorithms were carefully examined. Researchers often employ LBP characteristics because they are effective and competent for face expression recognition. Cohn This study makes use of the MATLAB programming language and the Kanade database. Splitting the face into smaller areas allows for the extraction of histograms and Local Binary Patterns (LBP), which are then combined into a single feature vector. When comparing pictures, this feature vector—which provides an orderly depiction of the face—is useful. This thesis proposes two Asymmetric Region LBP (ARLBP) and Extended LBP (EARLBP) operators, Modified Convolution (MC) operation and a heuristic localization method. These operators and other suggested methods were tested in various configurations, considering elements, for example, the presence or nonappearance of localization and registration problems, the reliance on the individual or their independence, the size of the matrices, the quantity of accessible LBP-based codes, and the operator scales. These histogram feature vectors based on LBP were used to setup the FR system in verification mode using the Eigen face technique. Support Vector Machine (SVM) mode for multi-class facial expression categorization was set up in the FER system. |
| AUTHOR | POONAM SHARMA, ARVIND SHARMA, DR HEMANT VIJAYVERGIA Research Scholar, Dept. of Digital Communication, Govt. Women Engineering College, Ajmer, India Assistant Professor, Dept. of Electronics and Communication, Govt. Women Engineering College, Ajmer, India |
| VOLUME | 180 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1401070 |
| pdf/70_Methods for Facial Expression Recognition with Applications in Challenging Situations.pdf | |
| KEYWORDS | |
| References | 1. Face Recognition Using DRLBP and SIFT Feature Extraction M. Sushama E.Rajinikanth 2018 International Conference on Communication and Signal Processing (ICCSP) 2. Representation of Pose Invariant Face Images Using SIFT Descriptors Nthabiseng Mokoena ; Kishor Nair2018 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD) 3. PNN-SIFT: An enhanced face recognition and classification system in image processing W. Sylvia Lilly Jebarani ; T. Kamalaharidharini 2017 4th International Conference on Electronics and Communication Systems (ICECS) 4. Robust face recognition and classification system based on SIFT and DCP techniques in image processing W. Sylvia Lilly Jebarani ; T. Kamalaharidharini 2017 International Conference on Intelligent Computing and Control (I2C2) 5. Image matching with SIFT feature Rajkumar N. Satare ; S. R. Khot 2018 2nd International Conference on Inventive Systems and Control (ICISC) 6. 6 B. Moghaddam, C. Nastar and A. Pentland, “A Bayesian similarity measure for deformable image matching,” Image and Vision Computing, vol. 19, no. 5, pp. 235-244, 2001. 7. B. Shan, “A Novel Image Correlation Matching Approach,” JMM, vol. 5, no. 3, 2010. 8. David G Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision, vol.50, No. 2, 2004, pp.91-110. 9. 9.E. Karami, M. Shehata, A. Smith, “Image Identification Using SIFT Algorithm: Performance Analysis Against Different Image 10. Deformations,” in Proceedings of the 2015 Newfoundland Electrical and Computer Engineering Conference,St. john’s, Canada, November, 2015. 11. 10 M. Güzel, 'A Hybrid Feature Extractor using Fast Hessian Detector and SIFT', Technologies, vol. 3, no. 2, pp. 103-110, 2015. 12. Liang-Chi Chiu, Tian-Sheuan Chang, Jiun-Yen Chen and N. Chang, 'Fast SIFT Design for Real-Time Visual Feature Extraction', IEEE Trans. On Image Process., vol. 22, no. 8, pp. 3158-3167, 2013. |
