Heterogeneous Specular and Diffuse 3-D Surface Approximation for Face Recognition Across Pose

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Title Heterogeneous Specular and Diffuse 3-D Surface Approximation for Face Recognition Across Pose
Author Zhang, Paul; Gao, Yongsheng
Journal Name IEEE Transactions on Information Forensics and Security
Year Published 2012
Place of publication United States
Publisher IEEE Signal Processing Society
Abstract This paper proposes a novel heterogeneous specular and diffuse (HSD) 3-D surface approximation which considers spatial variability of specular and diffuse reflections in face modelling and recognition. Traditional 3-D face modelling and recognition methods constrain human faces with either the Lambertian assumption or the homogeneity assumption, resulting in suboptimal shape and texture models. The proposed HSD approach allows both specular and diffuse reflectance coefficients to vary spatially to better accommodate surface properties of real human faces. From a small number of face images of a person under different lighting conditions, 3-D shape and surface reflectivity property are estimated using a localized stochastic optimization method. The resultant personalized 3-D face model is used to render novel gallery views under different poses for recognition across pose. The proposed approach is evaluated on both synthetic and real face datasets and benchmarked against the state-of-the-art approaches. Experimental results demonstrated that it can achieve a higher level of performances in modelling accuracy, algorithm reliability, and recognition accuracy, which suggests that face modelling and recognition beyond the Lambertian and homogeneity assumptions is a feasible and better solution towards pose-invariant face recognition.
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.1109/TIFS.2011.2170068
Volume 7
Issue Number 2
Page from 506
Page to 517
ISSN 1556-6013
Date Accessioned 2012-06-18
Language en_US
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Computer Vision; Pattern Recognition and Data Mining
URI http://hdl.handle.net/10072/47081
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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