Niranjan Samudre
Papers
1
Total Citations
32
H-Index
1
About
Niranjan Samudre is a computer vision researcher whose work bridges deep learning and affective computing, with a focus on real-time human-computer interaction. His most-cited paper, "Real-Time Convolutional Neural Networks for Emotion and Gender Classification" (2024, 32 citations), introduces a lightweight CNN architecture that simultaneously recognizes six basic emotions—happiness, sorrow, anger, fear, surprise, and disgust—alongside gender from facial images. This contribution addresses a critical need for efficient, deployable models in resource-constrained environments, enabling applications in adaptive user interfaces, mental health monitoring, and security systems. By optimizing feature extraction for real-time performance without sacrificing accuracy, Samudre’s work has influenced subsequent research in multi-task facial analysis. His approach demonstrates how compact neural networks can achieve practical utility in dynamic settings, earning recognition for its balance of speed and reliability. Samudre’s ongoing research continues to explore robust facial expression recognition under varied conditions, positioning him as a rising voice in the intersection of computer vision and human-centered AI.
Research Focus
Key Achievements
Top Papers
- 1