Narendra Choudhari
Papers
2
Total Citations
5
H-Index
2
About
Narendra Choudhari’s research centers on computer vision and human-computer interaction, with a primary focus on automatic facial expression recognition. His work addresses the fundamental challenge of deriving effective facial representations from images to enable machines to interpret human emotions. In his most-cited paper, “Facial expression representation and classification using LBP, 2DPCA and their combination” (2014, 3 citations), Choudhari proposed a novel hybrid approach that combines Local Binary Patterns (LBP) with Two-Dimensional Principal Component Analysis (2DPCA), demonstrating improved accuracy in classifying facial expressions. His related study, “Significance of facial features in performance of automatic facial expression recognition” (2014, 2 citations), systematically evaluates which facial regions contribute most to recognition performance, incorporating face detection methods to refine the analysis. Though early in his citation impact, Choudhari’s contributions are significant for advancing robust, real-world applications in robotics and HCI. His work provides foundational insights into feature selection and dimensionality reduction for emotion-aware systems, offering practical pathways for more intuitive human-machine interfaces.
Research Focus
Key Achievements
Top Papers
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- 2