activity
20182021
most citedThe FaceChannel: A Light-weight Deep Neural Network for Facial Expression Recognition

16 citations · 33 across the 6 of their papers we have counts for

collaborators

11 papers

cs.CV20216 cited

Towards Fair Affective Robotics: Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognition

Ozgur Kara, Nikhil Churamani, Hatice Gunes

As affective robots become integral in human life, these agents must be able to fairly evaluate human affective expressions without discriminating against specific demographic grou…

cs.CV20202 cited

Spatio-Temporal Analysis of Facial Actions using Lifecycle-Aware Capsule Networks

Nikhil Churamani, Sinan Kalkan, Hatice Gunes

Most state-of-the-art approaches for Facial Action Unit (AU) detection rely upon evaluating facial expressions from static frames, encoding a snapshot of heightened facial activity…

cs.CV2020

The FaceChannel: A Fast & Furious Deep Neural Network for Facial Expression Recognition

Pablo Barros, Nikhil Churamani, Alessandra Sciutti

Current state-of-the-art models for automatic Facial Expression Recognition (FER) are based on very deep neural networks that are effective but rather expensive to train. Given the…

cs.CV2020

CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions

Vincenzo Lomonaco, Lorenzo Pellegrini, Pau Rodriguez +12

In the last few years, we have witnessed a renewed and fast-growing interest in continual learning with deep neural networks with the shared objective of making current AI systems…

cs.HC20209 cited

Creating a Robot Coach for Mindfulness and Wellbeing: A Longitudinal Study

Indu P. Bodala, Nikhil Churamani, Hatice Gunes

Social robots are starting to become incorporated into daily lives by assisting in the promotion of physical and mental wellbeing. This paper investigates the use of social robots…

cs.CV2020

Continual Learning for Affective Computing

Nikhil Churamani

Real-world application requires affect perception models to be sensitive to individual differences in expression. As each user is different and expresses differently, these models…