Harsh Raperia
Papers
1
Total Citations
32
H-Index
1
About
Harsh Raperia is a rising researcher in computer vision and human-computer interaction, with a focused expertise in deep learning for facial analysis. His most-cited work, "Real-Time Convolutional Neural Networks for Emotion and Gender Classification" (2024), has already garnered 32 citations, reflecting its timely impact. In this paper, Raperia introduces a specialized CNN architecture that efficiently extracts features from facial images to simultaneously classify six basic emotions—happiness, sorrow, anger, fear, surprise, and disgust—along with gender. This dual-task approach addresses a critical need for real-time, accurate affective computing in applications ranging from responsive user interfaces to mental health monitoring. By balancing computational efficiency with classification accuracy, his contribution advances the practical deployment of emotion recognition systems. Raperia’s work stands out for its potential to enhance human-machine interaction, making technology more empathetic and context-aware. As an emerging voice in his field, his research bridges the gap between theoretical deep learning models and real-world usability, marking him as a scholar to watch in the evolving landscape of intelligent visual systems.
Research Focus
Key Achievements
Top Papers
- 1