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Tripti Goel

Work place: Department of Electronics and Communication Engineering, Bhagat Phool Singh Mahila Vishwavidyalaya, Khanpur Kalan, Sonepat, Haryana, India

E-mail: triptigoel83@gmail.com

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Research Interests: Image Processing, Image Manipulation, Image Compression, Pattern Recognition

Biography

Tripti Goel is Assistant Professor in National Institute of Technology, New Delhi, India. Prior to joining National Institute of Technology, she worked as Assistant Professor in the department of ECE at Guru Premsukh Memorial College of Engineering, Affiliated by Guru Gobind Singh Indraprastha University New Delhi, India from Jan. 2009 to Jan. 2015. She was with department of ECE at Bhagwan Mahaveer Institute of Engineering and technology, Sonepat, Haryana, India from 2008 to 2009. She received his B. Tech. degree in ECE from Hindu College of Engineering, Sonepat, Haryana , India, in 2004; M.E from Chhotu Ram State College of Engineering, Sonepat, Haryana, India, in 2008. She has published many research papers in refereed International Journals and Conferences and reviewed many research papers of International Journals and Conferences including IEEE explore, Inderscience and Springer. Her area of research includes digital image processing, soft computing and pattern recognition.

Author Articles
Pose Normalization based on Kernel ELM Regression for Face Recognition

By Tripti Goel Vijay Nehra Virendra P. Vishwakarma

DOI: https://doi.org/10.5815/ijigsp.2017.05.07, Pub. Date: 8 May 2017

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.

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