Papers
5
Total Citations
33
H-Index
3
About
Sumeet Saurav is a computer vision researcher whose work centers on facial expression recognition, traffic sign recognition, and human-robot interaction. His major contributions include developing efficient, real-time methods for analyzing human behavior and environmental cues. His most cited work, "Fast facial expression recognition using Boosted Histogram of Oriented Gradient (BHOG) features" (12 citations), introduced a lightweight yet powerful feature extraction technique that balances speed and accuracy. He further advanced this area with attention-guided and dual-channel deep convolutional neural networks for recognizing expressions in unconstrained, real-world settings—pushing the boundaries of human-computer and human-robot interaction. In the domain of autonomous driving, his work on histogram of oriented gradients-based reduced features for traffic sign recognition (11 citations) provides a robust solution for assisting drivers and autonomous vehicles. Most recently, Saurav has ventured into industrial applications, creating a multimodal dataset for monitoring operator engagement and object interactions in complex workflows. His research consistently bridges the gap between algorithmic efficiency and practical deployment, making him a notable contributor to applied computer vision.
Research Focus
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