Computer Vision – ECCV 2008

Volume 5303 of the series Lecture Notes in Computer Science pp 72-85

Face Alignment Via Component-Based Discriminative Search

  • Lin LiangAffiliated withMicrosoft Research Asia
  • , Rong XiaoAffiliated withMicrosoft Research Asia
  • , Fang WenAffiliated withMicrosoft Research Asia
  • , Jian SunAffiliated withMicrosoft Research Asia

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In this paper, we propose a component-based discriminative approach for face alignment without requiring initialization. Unlike many approaches which locally optimize in a small range, our approach searches the face shape in a large range at the component level by a discriminative search algorithm. Specifically, a set of direction classifiers guide the search of the configurations of facial components among multiple detected modes of facial components. The direction classifiers are learned using a large number of aligned local patches and misaligned local patches from the training data. The discriminative search is extremely effective and able to find very good alignment results only in a few (2~3) search iterations. As the new approach gives excellent alignment results on the commonly used datasets (e.g., AR [18], FERET [21]) created under-controlled conditions, we evaluate our approach on a more challenging dataset containing over 1,700 well-labeled facial images with a large range of variations in pose, lighting, expression, and background. The experimental results show the superiority of our approach on both accuracy and efficiency.