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M3 CSR Multi-view, multi-scale and multi-component cascade shape regression.pdf
Image and Vision Computing 47 (2016) 19–26
Contents lists available at ScienceDirect
Image and Vision Computing
journal homepage: /locate/imavis
M3 CSR: Multi-view, multi-scale and multi-component cascade shape regression☆
Jiankang Deng a, Qingshan Liu a,?, Jing Yang a, Dacheng Tao b
a B-DAT Laboratory, School of Information and Control, Nanjing University of Information and Technology, Nanjing 210044, China b QCIS Laboratory, Faculty of Engineering and Information Technology, University of Technology Sydney, 81 Broadway Street, Ultimo, NSW 2007, Australia
article info
Article history: Received 27 February 2015 Received in revised form 17 August 2015 Accepted 30 November 2015 Available online 15 December 2015
Keywords: Face alignment Cascade shape regression Multi-view Multi-scale Multi-component
abstract
Automatic face alignment is a fundamental step in facial image analysis. However, this problem continues to be challenging due to the large variability of expression, illumination, occlusion, pose, and detection drift in the realworld face images. In this paper, we present a multi-view, multi-scale and multi-component cascade shape regression (M3CSR) model for robust face alignment. Firstly, face view is estimated according to the deformable facial parts for learning view speci?ed CSR, which can decrease the shape variance, alleviate the drift of face detection and accelerate shape convergence. Secondly, multi-scale HoG features are used as the shape-index features to incorporate local structure information implicitly, and a multi-scale optimization strategy is adopted to avoid trapping in local optimum. Finally, a component-based shape re?nement process is developed to further improve the performance of face alignment. Extensive experiments on the IBUG dataset and the 300-W challenge dataset demonstrate the superiority of the proposed method over the state-of-the-art methods.
? 2015 Elsevier B.V. All rights reserved.
1. Introduction
Automatic facial landmark local
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