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 | Facial Expression Recognition Based on Local Binary Patterns |
|---|---|
| ABSTRACT | The 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. |
| 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.2026.1401069 |
| pdf/69_Facial Expression Recognition Based on Local Binary Patterns.pdf | |
| KEYWORDS | |
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