|Supervised Descent Method based on Appearance and Shape for Face Alignment|
|Conference Name||Proceedings of IEEE Service Operations and Logistics, and Informatics (SOLI)|
Regression approaches have been recently shown to achieve state-of-the-art performance for face alignment. As a general optimization problem, face alignment is approximately solved by learning a series of mapping functions from local appearance to the coordinates increment of the pixels to detect. There have been extensive studies and continuous improvements have been made in recent years. However, most of the existing methods only rely on the current facial texture in every iteration.
It is unreliable to only rely on local appearance information when facial landmarks are partially occluded in unconstrained scenarios.
In this paper, a modified supervised descent method is proposed to settle the issue, utilizing both appearance and shape information in learning regression functions. Hence, we call it asSDM.
The major contribution of our proposed method is to jointly capture shape and local appearance in cascade regression framework.
We evaluate the performance of the proposed method on different data sets and the experimental results on benchmark databases demonstrate that our proposed method outperforms previous work for facial landmark detection.
|Affiliation||Institute of Automation Chinese Academy of Sciences|
|Cheng，Yi. Supervised Descent Method based on Appearance and Shape for Face Alignment[C],2016.|
|Files in This Item:||Download All|
|Supervised Descent M（1231KB）||会议论文||开放获取||CC BY-NC-SA||View Download|
|Recommend this item|
|Export to Endnote|
|Similar articles in Google Scholar|
|Similar articles in Baidu academic|
|Similar articles in Bing Scholar|
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.