1 code implementation • 4 Aug 2023 • Ravikiran Parameshwara, Ibrahim Radwan, Akshay Asthana, Iman Abbasnejad, Ramanathan Subramanian, Roland Goecke
Whilst deep learning techniques have achieved excellent emotion prediction, they still require large amounts of labelled training data, which are (a) onerous and tedious to compile, and (b) prone to errors and biases.
no code implementations • 23 Jul 2023 • Monika Gahalawat, Raul Fernandez Rojas, Tanaya Guha, Ramanathan Subramanian, Roland Goecke
While depression has been studied via multimodal non-verbal behavioural cues, head motion behaviour has not received much attention as a biomarker.
no code implementations • 12 Jun 2023 • Soujanya Narayana, Ibrahim Radwan, Ravikiran Parameshwara, Iman Abbasnejad, Akshay Asthana, Ramanathan Subramanian, Roland Goecke
Whilst a majority of affective computing research focuses on inferring emotions, examining mood or understanding the \textit{mood-emotion interplay} has received significantly less attention.
no code implementations • 20 Feb 2023 • Surbhi Madan, Monika Gahalawat, Tanaya Guha, Roland Goecke, Ramanathan Subramanian
We explore the efficacy of multimodal behavioral cues for explainable prediction of personality and interview-specific traits.
1 code implementation • 21 Feb 2022 • Ravikiran Parameshwara, Soujanya Narayana, Murugappan Murugappan, Ramanathan Subramanian, Ibrahim Radwan, Roland Goecke
Employing traditional machine learning and deep learning methods, we explore (a) dimensional and categorical emotion recognition, and (b) PD vs HC classification from emotional EEG signals.
no code implementations • 22 Jun 2020 • Harshit Malik, Hersh Dhillon, Roland Goecke, Ramanathan Subramanian
Modeling hirability as a discrete/continuous variable with the \emph{big-five} personality traits as predictors, we utilize (a) apparent personality annotations, and (b) personality estimates obtained via audio, visual and textual cues for hirability prediction (HP).
1 code implementation • ICCV 2019 • Aamir Mustafa, Salman Khan, Munawar Hayat, Roland Goecke, Jianbing Shen, Ling Shao
Deep neural networks are vulnerable to adversarial attacks, which can fool them by adding minuscule perturbations to the input images.
Ranked #7 on Adversarial Defense on CIFAR-10
no code implementations • 23 Aug 2018 • Abhinav Dhall, Amanjot Kaur, Roland Goecke, Tom Gedeon
This paper details the sixth Emotion Recognition in the Wild (EmotiW) challenge.
no code implementations • CVPR 2017 • Munawar Hayat, Salman H. Khan, Naoufel Werghi, Roland Goecke
We validate the proposed scheme on template based unconstrained face identification.
no code implementations • 12 Oct 2016 • Xiaohua Huang, Abhinav Dhall, Xin Liu, Guoying Zhao, Jingang Shi, Roland Goecke, Matti Pietikainen
We fuse face, upperbody and scene information for robustness of GER against the challenging environments.
no code implementations • 21 Dec 2015 • O. V. Ramana Murthy, Roland Goecke
In this study, the influence of objects is investigated in the scenario of human action recognition with large number of classes.
no code implementations • 3 Dec 2015 • Ibrahim Radwan, Abhinav Dhall, Roland Goecke
The proposed method handles occlusions during the inference process by identifying overlapping regions between different sub-trees and introducing a penalty term for overlapping parts.