scholarly journals Pose Normalization via Learned 2D Warping for Fully Automatic Face Recognition

2011 ◽  
Author(s):  
Akshay Asthana ◽  
Michael Jones ◽  
Tim Marks ◽  
Kinh Tieu ◽  
Roland Goecke
Keyword(s):  
Face Recognition ◽  
Pose Normalization ◽  
Fully Automatic

scholarly journals Fully automatic pose-invariant face recognition via 3D pose normalization

2011 ◽  
Author(s):  
Akshay Asthana ◽  
Tim K. Marks ◽  
Michael J. Jones ◽  
Kinh H. Tieu ◽  
MV Rohith
Keyword(s):  
Face Recognition ◽  
Pose Normalization ◽  
Fully Automatic

Continuous Pose Normalization for Pose-Robust Face Recognition

2012 ◽  
Vol 19 (11) ◽  
pp. 721-724 ◽  
Author(s):  
Liu Ding ◽  
Xiaoqing Ding ◽  
Chi Fang
Keyword(s):  
Face Recognition ◽  
Pose Normalization ◽  
Robust Face Recognition

Robust pose normalization for face recognition under varying views

2015 ◽  
Author(s):  
Biao Wang ◽  
Xuetao Feng ◽  
Lujin Gong ◽  
Hao Feng ◽  
Wonjun Hwang ◽  
...  
Keyword(s):  
Face Recognition ◽  
Pose Normalization

Unconstrained Face Recognition

2018 ◽  
pp. 1640-1661
Author(s):  
Stefanos Zafeiriou ◽  
Irene Kotsia ◽  
Maja Pantic
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Partial Occlusion ◽  
Human Face ◽  
Facial Analysis ◽  
Identity Recognition ◽  
Unconstrained Face Recognition ◽  
Fully Automatic ◽  
Unconstrained Face ◽  
Appearance Changes

The human face is the most well-researched object in computer vision, mainly because (1) it is a highly deformable object whose appearance changes dramatically under different poses, expressions, and, illuminations, etc., (2) the applications of face recognition are numerous and span several fields, (3) it is widely known that humans possess the ability to perform, extremely efficiently and accurately, facial analysis, especially identity recognition. Although a lot of research has been conducted in the past years, the problem of face recognition using images captured in uncontrolled environments including several illumination and/or pose variations still remains open. This is also attributed to the existence of outliers (such as partial occlusion, cosmetics, eyeglasses, etc.) or changes due to age. In this chapter, the authors provide an overview of the existing fully automatic face recognition technologies for uncontrolled scenarios. They present the existing databases and summarize the challenges that arise in such scenarios and conclude by presenting the opportunities that exist in the field.

Unconstrained Face Recognition

2014 ◽  
pp. 16-37 ◽  
Author(s):  
Stefanos Zafeiriou ◽  
Irene Kotsia ◽  
Maja Pantic
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Partial Occlusion ◽  
Human Face ◽  
Facial Analysis ◽  
Identity Recognition ◽  
Unconstrained Face Recognition ◽  
Fully Automatic ◽  
Unconstrained Face ◽  
Appearance Changes

The human face is the most well-researched object in computer vision, mainly because (1) it is a highly deformable object whose appearance changes dramatically under different poses, expressions, and, illuminations, etc., (2) the applications of face recognition are numerous and span several fields, (3) it is widely known that humans possess the ability to perform, extremely efficiently and accurately, facial analysis, especially identity recognition. Although a lot of research has been conducted in the past years, the problem of face recognition using images captured in uncontrolled environments including several illumination and/or pose variations still remains open. This is also attributed to the existence of outliers (such as partial occlusion, cosmetics, eyeglasses, etc.) or changes due to age. In this chapter, the authors provide an overview of the existing fully automatic face recognition technologies for uncontrolled scenarios. They present the existing databases and summarize the challenges that arise in such scenarios and conclude by presenting the opportunities that exist in the field.

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