• English
  • Deutsch
  • Log In
    Password Login
    Research Outputs
    Fundings & Projects
    Researchers
    Institutes
    Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Konferenzschrift
  4. Impact of Head Pose Angles on Face Image Quality and Recognition Performance
 
  • Details
  • Full
Options
2026
Conference Paper
Title

Impact of Head Pose Angles on Face Image Quality and Recognition Performance

Abstract
The head pose is a critical influencing factor for the utility of a face image in face recognition systems. For reference face images such as passport photographs, the head pose is required to be frontal. For probe images, the question arises as to how much deviation from a frontal head pose would be tolerable without compromising recognition performance. Relaxed head pose requirements can speed up the process of capturing single-use probe images, e.g., at border checkpoints, as adopting a precise frontal pose can take several attempts. Our work validates the threshold values based on the head pose angles computed using the Open Source Face Image Quality (OFIQ) software.
Author(s)
Kurz, Alexander
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Carnap, Jacob
TU Darmstadt, Fachgebiet Graphisch-Interaktive Systeme  
Henniger, Olaf  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Mainwork
ICPRAM 2026, 15th International Conference on Pattern Recognition Applications and Methods. Proceedings  
Project(s)
Next Generation Biometric Systems  
Next Generation Biometric Systems  
Funder
Bundesministerium für Bildung und Forschung  
Hessen, Ministerium für Wissenschaft und Kunst  
Conference
International Conference on Pattern Recognition Applications and Methods 2026  
Open Access
File(s)
Download (5.13 MB)
Rights
CC BY-NC-ND 4.0: Creative Commons Attribution-NonCommercial-NoDerivatives
DOI
10.5220/0014319800004067
10.24406/publica-8036
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • Branche: Infrastructure and Public Services

  • Research Line: Computer vision (CV)

  • Research Line: Machine learning (ML)

  • LTA: Machine intelligence, algorithms, and data structures (incl. semantics)

  • Biometrics

  • Face recognition

  • Image quality

  • ATHENE

  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Contact
© 2024