Face Alignment Via Component-Based Discriminative Search

In this paper, we propose a component-based discriminative approach for face alignment without requiring initialization1 . 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 (23) 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 ...

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Lin Liang, Rong Xiao, Fang Wen, Jian Sun
• Keywords: Component-based Discriminative Approach, Computer Vision, Direction Classifiers, Discriminative Search Algorithm, ECCV 2008, Facial Components, Well-labeled Facial Images