Sunny Bagga

Dwarkadas J. Sanghvi College of Engineering

Papers

Total Citations

H-Index

About

Sunny Bagga’s research focuses on advancing automatic facial expression recognition, a critical area for human-computer interaction and robotics. Her major contributions lie in developing effective facial representations and classification methods. In her most-cited work, she proposed a novel approach combining Local Binary Patterns (LBP) and Two-Dimensional Principal Component Analysis (2DPCA) for facial expression representation and classification, achieving robust performance in recognizing emotions. Her second key study systematically analyzed the significance of various facial features—such as eyes, mouth, and eyebrows—in improving recognition accuracy, providing insights into which regions are most informative for expression detection. Though her citation counts are modest (3 and 2 citations respectively), her work contributes foundational knowledge to the field, particularly in feature extraction and dimensionality reduction techniques. Bagga’s research underscores the importance of integrating texture and holistic features for reliable facial analysis, offering practical guidance for developing more intuitive and responsive interactive systems. Her efforts help bridge the gap between raw facial data and meaningful emotional interpretation, supporting applications from virtual assistants to affective computing.