Component Localization in Face Alignment

Face alignment is a significant problem in the processing of face image, and Active Shape Model (ASM) is a popular technology for this problem. However, the initiation of the alignment strongly affects the performance of ASM. If the initiation of alignment is bad, the iteration of ASM optimization will be stuck in a local minima, and the alignment will fail. In this paper, we propose a novel approach to improve ASM by building the classifiers of the face components. We design the SVM classifiers for eyes, mouth and nose, and we use Speeded Up Robust Features(SURF) and Local Binary Pattern(LBP) feature to describe the components which are discriminative for the components than Haar-like features. The face components are firstly located by the classifiers and they indicate the initiation of the alignment. Our approach can make the iterations of ASM optimization converge fast and with the less errors. We evaluate our approach on the frontal views of upright faces of IMM dataset. The experimental results have shown that our approach outperforms the original ASM in terms of efficiency and accuracy.

@article{Qu2011,
  title     = {Component Localization in Face Alignment},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {1},
  number    = {1},
  year      = {2011},
  publisher = {The Science and Information Organization},
  note      = {Special Issue on Image Processing and Analysis},
  author    = {Yanyun Qu and Tianzhu Fang and Yanyun Cheng and Han Liu},
  doi       = {10.14569/SpecialIssue.2011.010109},
  url       = {https://doi.org/10.14569/SpecialIssue.2011.010109}
}