Automatic Partial Face Alignment in NIR Video Sequences

  • Jimei Yang
  • Shengcai Liao
  • Stan Z. Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


Face recognition with partial face images is an important problem in face biometrics. The necessity can arise in not so constrained environments such as in surveillance video, or portal video as provided in Multiple Biometrics Grand Challenge (MBGC). Face alignment with partial face images is a key step toward this challenging problem.

In this paper, we present a method for partial face alignment based on scale invariant feature transform (SIFT). We first train a reference model using holistic faces, in which the anchor points and their corresponding descriptor subspaces are learned from initial SIFT keypoints and the relationships between the anchor points are also derived. In the alignment stage, correspondences between the learned holistic face model and an input partial face image are established by matching keypoints of the partial face to the anchor points of the learned face model. Furthermore, shape constraint is used to eliminate outlier correspondences and temporal constraint is explored to find more inliers. Alignment is finally accomplished by solving a similarity transform. Experiments on the MBGC near infrared video sequences show the effectiveness of the proposed method, especially when PCA subspace, shape and temporal constraint are utilized.


Face Alignment Partial Faces SIFT MBGC 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jimei Yang
    • 1
    • 2
  • Shengcai Liao
    • 2
  • Stan Z. Li
    • 2
  1. 1.University of Science and Technology of ChinaHefeiChina
  2. 2.Center for Biometrics and Security Research, Institute of AutomationChinese Academy of SciencesBeijingChina

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