1 code implementation • 12 Mar 2024 • Saksham Checker, Nikhil Churamani, Hatice Gunes
In this paper, we present a novel FL benchmark that evaluates different strategies, using multi-label regression objectives, where each client individually learns to predict the social appropriateness of different robot actions while also sharing their learning with others.
no code implementations • 10 May 2023 • Nikhil Churamani, Tolga Dimlioglu, German I. Parisi, Hatice Gunes
Understanding human affective behaviour, especially in the dynamics of real-world settings, requires Facial Expression Recognition (FER) models to continuously adapt to individual differences in user expression, contextual attributions, and the environment.
no code implementations • 15 Mar 2021 • Nikhil Churamani, Ozgur Kara, Hatice Gunes
As Facial Expression Recognition (FER) systems become integrated into our daily lives, these systems need to prioritise making fair decisions instead of aiming at higher individual accuracy scores.
no code implementations • 15 Mar 2021 • 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 groups.
no code implementations • 17 Nov 2020 • Nikhil Churamani, Sinan Kalkan, Hatice Gunes
In real-world interactions, however, facial expressions are usually more subtle and evolve in a temporal manner requiring AU detection models to learn spatial as well as temporal information.
no code implementations • 14 Oct 2020 • Nikhil Churamani, Pablo Barros, Hatice Gunes, Stefan Wermter
Collaborative interactions require social robots to adapt to the dynamics of human affective behaviour.
no code implementations • 15 Sep 2020 • Pablo Barros, Nikhil Churamani, Alessandra Sciutti
We conclude our paper with an analysis of how FaceChannel learns and adapt the learned facial features towards the different datasets.
Facial Expression Recognition Facial Expression Recognition (FER)
1 code implementation • 14 Sep 2020 • Vincenzo Lomonaco, Lorenzo Pellegrini, Pau Rodriguez, Massimo Caccia, Qi She, Yu Chen, Quentin Jodelet, Ruiping Wang, Zheda Mai, David Vazquez, German I. Parisi, Nikhil Churamani, Marc Pickett, Issam Laradji, Davide Maltoni
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 more adaptive, efficient and autonomous.
no code implementations • 10 Jun 2020 • Nikhil Churamani
Real-world application requires affect perception models to be sensitive to individual differences in expression.
1 code implementation • 17 Apr 2020 • Pablo Barros, Nikhil Churamani, Alessandra Sciutti
Current state-of-the-art models for automatic FER are based on very deep neural networks that are difficult to train.
Facial Expression Recognition Facial Expression Recognition (FER)
no code implementations • 30 Aug 2019 • Pablo Barros, Nikhil Churamani, Angelica Lim, Stefan Wermter
In this paper, we propose a novel dataset composed of dyadic interactions designed, collected and annotated with a focus on measuring the affective impact that eight different stories have on the listener.
no code implementations • 14 Mar 2018 • Pablo Barros, Nikhil Churamani, Egor Lakomkin, Henrique Siqueira, Alexander Sutherland, Stefan Wermter
This paper is the basis paper for the accepted IJCNN challenge One-Minute Gradual-Emotion Recognition (OMG-Emotion) by which we hope to foster long-emotion classification using neural models for the benefit of the IJCNN community.
Human-Computer Interaction