5 citations · 8 across the 6 of their papers we have counts for
10 papers
Deep Insights of Learning based Micro Expression Recognition: A Perspective on Promises, Challenges and Research Needs
Monu Verma, Santosh Kumar Vipparthi, Girdhari Singh
Micro expression recognition (MER) is a very challenging area of research due to its intrinsic nature and fine-grained changes. In the literature, the problem of MER has been solve…
RARITYNet: Rarity Guided Affective Emotion Learning Framework
Monu Verma, Santosh Kumar Vipparthi
Inspired from the assets of handcrafted and deep learning approaches, we proposed a RARITYNet: RARITY guided affective emotion learning framework to learn the appearance features a…
Cross-Centroid Ripple Pattern for Facial Expression Recognition
Monu Verma, Prafulla Saxena, Santosh Kumar Vipparthi +1
In this paper, we propose a new feature descriptor Cross-Centroid Ripple Pattern (CRIP) for facial expression recognition. CRIP encodes the transitional pattern of a facial express…
One for All: An End-to-End Compact Solution for Hand Gesture Recognition
Monu Verma, Ayushi Gupta, santosh kumar Vipparthi
The HGR is a quite challenging task as its performance is influenced by various aspects such as illumination variations, cluttered backgrounds, spontaneous capture, etc. The conven…
AffectiveNet: Affective-Motion Feature Learningfor Micro Expression Recognition
Monu Verma, Santosh Kumar Vipparthi, Girdhari Singh
Micro-expressions are hard to spot due to fleeting and involuntary moments of facial muscles. Interpretation of micro emotions from video clips is a challenging task. In this paper…
Non-Linearities Improve OrigiNet based on Active Imaging for Micro Expression Recognition
Monu Verma, Santosh Kumar Vipparthi, Girdhari Singh
Micro expression recognition (MER)is a very challenging task as the expression lives very short in nature and demands feature modeling with the involvement of both spatial and temp…