Orthogonal discriminant vector for face recognition across pose

Jinghua Wang, Jia You, Qin Li, Yong Xu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

39 Citations (Scopus)


Recognizing face images across pose is one of the challenging tasks for reliable face recognition. This paper presents a new method to tackle this challenge based on orthogonal discriminant vector (ODV). The result of our theoretical analysis shows that an individual's probe image captured with a new pose can be represented by a linear combination of his/her gallery images. Based on this observation, in contrast to the conventional methods which model face images of different individuals on a single manifold, we propose to model face images of different individuals on different linear manifolds. The contribution of our approach includes: (1) to prove that the orthogonality to ODVs is a pose-invariant feature.; (2) to categorize each person with a set of ODVs, where his/her face images posses zero projections while other persons' images are characterized by maximum projections; (3) to define a metric to measure the distance between a face image and an ODV, classify the face images based on this metric. Our experimental results validate the feasibility of modeling the face images of different individuals on different linear manifolds. The proposed method achieves higher accuracy on face recognition and verification than the existing techniques.
Original languageEnglish
Pages (from-to)4069-4079
Number of pages11
JournalPattern Recognition
Issue number12
Publication statusPublished - 1 Dec 2012


  • Face manifold
  • Face recognition across pose
  • Orthogonal discriminant vector
  • Pattern classification

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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