no code implementations • 24 Mar 2022 • Saurabh Hinduja, Shaun Canavan, Liza Jivnani, Sk Rahatul Jannat, V Sri Chakra Kumar
In this paper we describe our approach to the arousal and valence track of the 3rd Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW).
no code implementations • 5 Oct 2021 • Md Taufeeq Uddin, Shaun Canavan
The quantified measurement of facial expressiveness is crucial to analyze human affective behavior at scale.
no code implementations • 15 Nov 2020 • Md Taufeeq Uddin, Shaun Canavan, Ghada Zamzmi
In this work, we address the importance of affect in automated pain assessment and the implications in real-world settings.
no code implementations • 28 Oct 2020 • Md Taufeeq Uddin, Shaun Canavan
The quantification of visual affect data (e. g. face images) is essential to build and monitor automated affect modeling systems efficiently.
no code implementations • 15 Oct 2020 • Saurabh Hinduja, Shaun Canavan, Saandeep Aathreya
In this paper we investigate the impact of action unit occurrence patterns on detection of action units.
no code implementations • 17 May 2020 • Diego Fabiano, Manikandan Jaishanker, Shaun Canavan
Considering this, we present an analysis of 3D facial data, action units, and physiological data as it relates to their impact on emotion recognition.
no code implementations • 17 May 2020 • Sk Rahatul Jannat, Diego Fabiano, Shaun Canavan, Tempestt Neal
Landmark localization is an important first step towards geometric based vision research including subject identification.
no code implementations • 17 May 2020 • Neilesh Sambhu, Shaun Canavan
In this paper, we propose to detect forged videos, of faces, in online videos.
no code implementations • 17 May 2020 • Saurabh Hinduja, Shaun Canavan
In this paper, we propose to detect facial action units (AU) using 3D facial landmarks.
no code implementations • 27 Jun 2019 • Khadija Zanna, Sayde King, Tempestt Neal, Shaun Canavan
This paper explores the identification of smartphone users when certain samples collected while the subject felt happy, upset or stressed were absent or present.
no code implementations • CVPR 2016 • Zheng Zhang, Jeff M. Girard, Yue Wu, Xing Zhang, Peng Liu, Umur Ciftci, Shaun Canavan, Michael Reale, Andy Horowitz, Huiyuan Yang, Jeffrey F. Cohn, Qiang Ji, Lijun Yin
The corpus further includes derived features from 3D, 2D, and IR (infrared) sensors and baseline results for facial expression and action unit detection.