International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 9, No. 5, 8 May 2017
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Pose Normalization based on Kernel ELM Regression for Face Recognition
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Author(s)
Index Terms
Face recognition, Kernel Extreme Learning Machine Regression, Pose normalization, Virtual Frontal View
Abstract
Pose variation is the one of the main difficulty faced by present automatic face recognition system. Due to the pose variations, feature vectors of the same person may vary more than inter person identity. This paper aims to generate virtual frontal view from its corresponding non frontal face image. The approach presented in this paper is based on the assumption of existence of an approximate mapping between the non frontal posed image and its corresponding frontal view. By calculating the mapping between frontal and posed image, the problem of estimating the frontal view will become the regression problem. In the present approach, non linear mapping, kernel extreme learning machine (KELM) regression is used to generate virtual frontal face image from its non frontal counterpart. Kernel ELM regression is used to compensate for the non linear shape of the face. The studies are performed on GTAV database with 5 posed images and compared with linear regression approach.
Cite This Paper
Tripti Goel, Vijay Nehra, Virendra P. Vishwakarma,"Pose Normalization based on Kernel ELM Regression for Face Recognition", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.9, No.5, pp.68-75, 2017. DOI: 10.5815/ijigsp.2017.05.07
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