Search Results for author: Ramanathan Subramanian

Found 15 papers, 3 papers with code

Automated Parkinson's Disease Detection and Affective Analysis from Emotional EEG Signals

1 code implementation21 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.

EEG Emotion Recognition

Outlier-based Autism Detection using Longitudinal Structural MRI

no code implementations21 Feb 2022 Devika K, Venkata Ramana Murthy Oruganti, Dwarikanath Mahapatra, Ramanathan Subramanian

Among other findings, metrics employed for model training as well as reconstruction loss computation impact detection performance, and the coronal modality is found to best encode structural information for ASD detection.

Outlier Detection

Head Matters: Explainable Human-centered Trait Prediction from Head Motion Dynamics

no code implementations15 Dec 2021 Surbhi Madan, Monika Gahalawat, Tanaya Guha, Ramanathan Subramanian

We demonstrate the utility of elementary head-motion units termed kinemes for behavioral analytics to predict personality and interview traits.

FakeBuster: A DeepFakes Detection Tool for Video Conferencing Scenarios

no code implementations9 Jan 2021 Vineet Mehta, Parul Gupta, Ramanathan Subramanian, Abhinav Dhall

This paper proposes a new DeepFake detector FakeBuster for detecting impostors during video conferencing and manipulated faces on social media.

Face Swapping

Characterizing Hirability via Personality and Behavior

no code implementations22 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).

The eyes know it: FakeET -- An Eye-tracking Database to Understand Deepfake Perception

no code implementations12 Jun 2020 Parul Gupta, Komal Chugh, Abhinav Dhall, Ramanathan Subramanian

We present \textbf{FakeET}-- an eye-tracking database to understand human visual perception of \emph{deepfake} videos.

EEG Face Swapping

Looking Beyond a Clever Narrative: Visual Context and Attention are Primary Drivers of Affect in Video Advertisements

no code implementations14 Aug 2018 Abhinav Shukla, Harish Katti, Mohan Kankanhalli, Ramanathan Subramanian

Contrary to the popular notion that ad affect hinges on the narrative and the clever use of linguistic and social cues, we find that actively attended objects and the coarse scene structure better encode affective information as compared to individual scene objects or conspicuous background elements.

AVEID: Automatic Video System for Measuring Engagement In Dementia

no code implementations21 Dec 2017 Viral Parekh, Pin Sym Foong, Shendong Zhao, Ramanathan Subramanian

Engagement in dementia is typically measured using behavior observational scales (BOS) that are tedious and involve intensive manual labor to annotate, and are therefore not easily scalable.

An EEG-based Image Annotation System

no code implementations7 Nov 2017 Viral Parekh, Ramanathan Subramanian, Dipanjan Roy, C. V. Jawahar

The success of deep learning in computer vision has greatly increased the need for annotated image datasets.


Evaluating Crowdsourcing Participants in the Absence of Ground-Truth

no code implementations30 May 2016 Ramanathan Subramanian, Romer Rosales, Glenn Fung, Jennifer Dy

Given a supervised/semi-supervised learning scenario where multiple annotators are available, we consider the problem of identification of adversarial or unreliable annotators.

Uncovering Interactions and Interactors: Joint Estimation of Head, Body Orientation and F-Formations From Surveillance Videos

no code implementations ICCV 2015 Elisa Ricci, Jagannadan Varadarajan, Ramanathan Subramanian, Samuel Rota Bulo, Narendra Ahuja, Oswald Lanz

We present a novel approach for jointly estimating tar- gets' head, body orientations and conversational groups called F-formations from a distant social scene (e. g., a cocktail party captured by surveillance cameras).

SALSA: A Novel Dataset for Multimodal Group Behavior Analysis

no code implementations23 Jun 2015 Xavier Alameda-Pineda, Jacopo Staiano, Ramanathan Subramanian, Ligia Batrinca, Elisa Ricci, Bruno Lepri, Oswald Lanz, Nicu Sebe

Studying free-standing conversational groups (FCGs) in unstructured social settings (e. g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels.

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