Estimation of Face Pose Orientation Using Model-Based Approach

  • M. Annalakshmi
  • S. M. Mansoor Roomi
  • M. Parisa BehamEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 65)


In the domain of computer vision and pattern recognition, though there are numerous methods for face recognition, it is still remaining as a very challenging problem in real life applications. Face detection and recognition suffer from many problems which are caused by the variations in orientation, size, illumination, expression, and poses. This paper mainly revolves around face detection and oriented pose identification. The state-of-the-art Constrained Local Model (CLM) is applied to detect the face from any wild facial image. The extracted feature points are used to segregate the dominant parts of faces. From the dominant feature points, nose tip and eye points have been identified. Applying the geometrical parameters between the nose tip and eye points, the pose orientation of the wild face has been identified. This method is very simple and accurate. The performance evaluation has been done on unconstrained Essex database and internal wild database collected from internet.


CLM model CLM search Segregation Pose estimation Geometrical parameters 


  1. 1.
    Asthana A, Marks TK, Jones MJ, Tieu KHT, Rohith MV (2011) Fully automatic pose-invariant face recognition via 3D pose normalization, 2011, TR2011-074Google Scholar
  2. 2.
    Asthana A, Zafeiriou S, Cheng S, Pantic M (2013) Robust discriminative response map fitting with constrained local model, CVPR, 2013Google Scholar
  3. 3.
    Zhu X, Ramanan D (2013) Face detection, pose estimation and landmark localization in the wild, CVPR, 2013Google Scholar
  4. 4.
    Yi D, Lei Z, Li SZ (2013) Towards pose robust face recognition, CVPR, 2013Google Scholar
  5. 5.
    Chu B, Romdhani S, Chen L (2014) 3D aided face recognition robust to expression and pose variations, 2014Google Scholar
  6. 6.
    Sarode JP, Anuse AD (2014) Face recognition under pose variation, 2014Google Scholar
  7. 7.
    Tai Y, Yang J, Zhang Y, Luo L, Qian J, Chen Y (2016) Face recognition with pose variation and misalignment via orthogonal procrustes regression. IEEE Trans Image Process 25(6)Google Scholar
  8. 8.
    Jones MJ, Viola PA (2003) Fast multi-view face detection. Technical Report TR2003-96, 2003, Mitsubishi Electric Research Laboratories, Cambridge, MAGoogle Scholar
  9. 9.
    The Essex Genealogist. Online database: American New England Historic Genealogical Society, 2011Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • M. Annalakshmi
    • 1
  • S. M. Mansoor Roomi
    • 2
  • M. Parisa Beham
    • 1
    Email author
  1. 1.Department of ECESethu Institute of TechnologyVirudhunagarIndia
  2. 2.Department of ECEThiagarajar College of EngineeringMaduraiIndia

Personalised recommendations