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A Novel Approach for Biometric Authentication System Using Ear, 2D Face and 3D Face Modalities Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Pose Invariant Face Recognition for New Born: Machine Learning Approach

Authors : Rishav Singh, Hari Om

Published in: Computational Intelligence in Data Mining—Volume 1

Publisher: Springer India

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Abstract

Pose is a natural and important covariate in case of newborn and face recognition across pose can troubleshoot the approaches dealing with uncooperative subjects like newborn, in which the full power of face recognition being a passive biometric technique requires to be implemented and utilized. To handle the large pose variation in newborn, we propose a pose-adaptive similarity method that uses pose-specific classifiers to deal with different combinatorial poses. A texture based face recognition method, Speed Up Robust Feature (SURF) transform, is used to compare the descriptor of testing (probe) face with given training (gallery) face descriptor. Probes executed on the face template data of newborn described here, offer comparative benefits towards affinity for pose variations and the proposed algorithm verdicts the rank 1 accuracy of 92.1 %, which demonstrates the strength of self learning even with single training face image of newborn.

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Metadata
Title
Pose Invariant Face Recognition for New Born: Machine Learning Approach
Authors
Rishav Singh
Hari Om
Copyright Year
2016
Publisher
Springer India
Book
Computational Intelligence in Data Mining—Volume 1

Print ISBN: 978-81-322-2732-8

Electronic ISBN: 978-81-322-2734-2

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-81-322-2734-2_4

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