Search
Publication Authors

Prof. Dr. Didier Stricker

Dr. Alain Pagani

Dr. Gerd Reis

Eric Thil

Keonna Cunningham

Dr. Oliver Wasenmüller

Dr. Muhammad Zeshan Afzal

Dr. Gabriele Bleser

Dr. Muhammad Jameel Nawaz Malik
Dr. Bruno Mirbach

Dr. Jason Raphael Rambach

Dr. Nadia Robertini

Dr. René Schuster

Dr. Bertram Taetz

Ahmed Aboukhadra

Sk Aziz Ali

Mhd Rashed Al Koutayni

Yuriy Anisimov

Jilliam Maria Diaz Barros

Ramy Battrawy
Hammad Butt

Mahdi Chamseddine

Steve Dias da Cruz
Fangwen Shu

Torben Fetzer

Ahmet Firintepe
Sophie Folawiyo

David Michael Fürst
Anshu Garg

Christiano Couto Gava
Suresh Guttikonda

Tewodros Amberbir Habtegebrial
Simon Häring

Khurram Azeem Hashmi
Henri Hoyez
Alireza Javanmardi

Jigyasa Singh Katrolia

Andreas Kölsch
Ganesh Shrinivas Koparde
Onorina Kovalenko

Stephan Krauß
Paul Lesur

Michael Lorenz

Dr. Markus Miezal

Mina Ameli

Nareg Minaskan Karabid

Mohammad Minouei

Pramod Murthy

Mathias Musahl

Peter Neigel

Manthan Pancholi
Mariia Podguzova

Praveen Nathan
Qinzhuan Qian
Rishav
Marcel Rogge
María Alejandra Sánchez Marín
Dr. Kripasindhu Sarkar

Alexander Schäfer

Pascal Schneider

Mohamed Selim

Tahira Shehzadi
Lukas Stefan Staecker

Yongzhi Su

Xiaoying Tan
Christian Witte

Yaxu Xie

Vemburaj Yadav

Dr. Vladislav Golyanik

Dr. Aditya Tewari

André Luiz Brandão
Publication Archive
New title
- ActivityPlus
- AlterEgo
- AR-Handbook
- ARVIDA
- Auroras
- AVILUSplus
- Be-greifen
- Body Analyzer
- CAPTURE
- COGNITO
- DAKARA
- Density
- DYNAMICS
- EASY-IMP
- Eyes Of Things
- iACT
- IMCVO
- IVMT
- LARA
- LiSA
- Marmorbild
- Micro-Dress
- Odysseus Studio
- On Eye
- OrcaM
- PAMAP
- PROWILAN
- ServiceFactory
- STREET3D
- SUDPLAN
- SwarmTrack
- TuBUs-Pro
- VIDETE
- VIDP
- VisIMon
- VISTRA
- You in 3D
Real-Time Head Pose Estimation Using Multi-Variate RVM on Faces in the Wild
Real-Time Head Pose Estimation Using Multi-Variate RVM on Faces in the Wild
Mohamed Selim, Alain Pagani, Didier Stricker
International Conference on Computer Analysis of Images and Patterns (CAIP-2015), 16th, September 2-4, Valletta, Malta
- Abstract:
- Various computer vision problems and applications rely on an accurate, fast head pose estimator. We model head pose estimation as a regression problem. We show that it is possible to use the appearance of the facial image as a feature which depicts the pose variations. We use a parametrized Multi-Variate Relevance Vector Machine (MVRVM) to learn the three rotation angles of the face (yaw, pitch, and roll). The input of the MVRVM is normalized mean pixel intensities of the face patches, and the output is the three head rotation angles. We evaluated our approach on the challenging YouTube faces dataset. We achieved a head pose estimation with an average error tolerance of 6.5 degrees in the yaw rotation angle, and less than 2.5 degrees in both the pitch and roll angles. The time taken in one prediction is 2-3 milliseconds, hence suitable for real-time applications.
- Keywords:
- Head Pose Estimation, Real-time, MVRVM, Faces in the Wild