139 citations · 148 across the 10 of their papers we have counts for
12 papers
JaxARC: A High-Performance JAX-based Environment for Abstraction and Reasoning Research
Aadam, Monu Verma, Mohamed Abdel-Mottaleb
The Abstraction and Reasoning Corpus (ARC) tests AI systems' ability to perform human-like inductive reasoning from a few demonstration pairs. Existing Gymnasium-based RL environme…
Efficient Neural Architecture Search for Emotion Recognition
Monu Verma, Murari Mandal, Satish Kumar Reddy +2
Automated human emotion recognition from facial expressions is a well-studied problem and still remains a very challenging task. Some efficient or accurate deep learning models hav…
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…
LEARNet Dynamic Imaging Network for Micro Expression Recognition
Monu Verma, Santosh Kumar Vipparthi, Girdhari Singh +1
Unlike prevalent facial expressions, micro expressions have subtle, involuntary muscle movements which are short-lived in nature. These minute muscle movements reflect true emotion…
EXPERTNet Exigent Features Preservative Network for Facial Expression Recognition
Monu Verma, Jaspreet Kaur Bhui, Santosh Vipparthi +1
Facial expressions have essential cues to infer the humans state of mind, that conveys adequate information to understand individuals actual feelings. Thus, automatic facial expres…
Region Based Extensive Response Index Pattern for Facial Expression Recognition
Monu Verma, Santosh. K. Vipparthi, Girdhari Singh
This paper presents a novel descriptor named Region based Extensive Response Index Pattern (RETRaIN) for facial expression recognition. The RETRaIN encodes the relation among the r…
QUEST: Quadriletral Senary bit Pattern for Facial Expression Recognition
Monu Verma, Prafulla Saxena, Santosh. K. Vipparthi +1
Facial expression has a significant role in analyzing human cognitive state. Deriving an accurate facial appearance representation is a critical task for an automatic facial expres…