Pose Invariant Face Recognition for New Born: Machine Learning Approach

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 410)

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.

Keywords

SURF Face recognition Machine learning Classification Regression 

Notes

Acknowledgments

The authors would like to thank Dr. Shrikant Tiwari (Department of Computer Science and Engineering, IIT (BHU) for providing database of newborn.

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Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringIndian School of MinesDhanbadIndia

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