Feedback cascade regression model for face alignment

Autor:	Yangyang Hao, Hengliang Zhu, Zhiwen Shao, Lizhuang Ma
Jazyk:	angličtina
Rok vydání:	2019
Předmět:	feedback cascade regression model face alignment cascade regression framework heavily occluded faces hidden landmarks unified initialisation Computer applications to medicine. Medical informatics R858-859.7 Computer software QA76.75-76.765
Zdroj:	IET Computer Vision, Vol 13, Iss 7, Pp 632-639 (2019)
Druh dokumentu:	article
ISSN:	1751-9640 1751-9632
DOI:	10.1049/iet-cvi.2018.5347
Popis:	Face alignment has made great progress in recent years and the cascade regression framework is one of the main contributors. However, the performance of this framework is unsatisfactory on heavily occluded faces or those far from the frontal pose. This is because regression is sensitive to hidden landmarks and unified initialisation can often lead to the method falling into local minima. The authors propose a new pipeline of salient‐to‐inner‐to‐all to progressively compute the locations of landmarks. Additionally, a feedback process is utilised to improve the robustness of regression. They bring out a pose‐invariant shape retrieval method to generate the discriminative initialisation. Experiments are performed on two benchmarks, and the experimental results demonstrate that the proposed method has a considerable improvement on the cascade regression model, and achieves favourable results compared with the state‐of‐the‐art deep learning‐based methods.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/d586b02cecb843e0af75746c0548d5a0 Zobrazit plný text záznamu Plný text View record in DOAJ