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Erschienen in:
Experimental Analysis on Big Data in IOT-Based Architecture Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Comparative Analysis of Frontal Face Recognition Using Radial Curves and Back Propagation Neural Network

verfasst von : Latasha Keshwani, Dnyandeo Pete

Erschienen in: Proceedings of the International Conference on Data Engineering and Communication Technology

Verlag: Springer Singapore

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Abstract

Person identification using face as a cue is one of the most prominent and robust technique. This paper presents 3D face recognition system using Radial curves and Back Propagation Neural Networks (BPNN). The face images used for experimentation are under various challenges like illumination, pose variation, expression and occlusions. The features of images are extracted using Eigen vectors. These features are compared using radial curves on the face starting from center of the face to the end of the face. Each corresponding curve is matched using Euclidean Distance classifier. The BPNN is used to train the features for face matching. The proposed algorithms are tested on ORL and DMCE database. The performance analysis is based on recognition rate accuracy of the system. The proposed radial curve system yields recognition rate accuracy of 100 % for images from the ORL database and 98 % for the images from DMCE database.

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Metadaten
Titel
Comparative Analysis of Frontal Face Recognition Using Radial Curves and Back Propagation Neural Network
verfasst von
Latasha Keshwani
Dnyandeo Pete
Copyright-Jahr
2017
Verlag
Springer Singapore
Buch
Proceedings of the International Conference on Data Engineering and Communication Technology

Print ISBN: 978-981-10-1677-6

Electronic ISBN: 978-981-10-1678-3

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-981-10-1678-3_32