Tripti Goel; Vijay Nehra; Virendra P. Vishwakarma

Pose Normalization based on Kernel ELM Regression for Face Recognition

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Author(s)

Tripti Goel ^1,* Vijay Nehra ¹ Virendra P. Vishwakarma ²

1. Department of Electronics and Communication Engineering, Bhagat Phool Singh Mahila Vishwavidyalaya, Khanpur Kalan, Sonepat, Haryana, India

2. University School of Information and Communication Technology, Guru Gobind Singh Indarprastha University, Dwarka, Delhi, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2017.05.07

Received: 11 Jan. 2017 / Revised: 22 Feb. 2017 / Accepted: 6 Apr. 2017 / Published: 8 May 2017

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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International Journal of Image, Graphics and Signal Processing (IJIGSP)