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Erschienen in:
Human Action Recognition Using Trajectory-Based Spatiotemporal Descriptors Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Feature Optimality-Based Semi-supervised Face Recognition Approach

verfasst von : Taqdir, Renu dhir

Erschienen in: Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications

Verlag: Springer Singapore

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Abstract

In this paper, a novel approach is proposed that cope with challenges such as illuminations, expressions, poses, and occlusions. The proposed methodology is a non-domination-based optimization technique with a semi-supervised classifier for recognizing a known and unknown face based on different scenarios. The classification is a robust method attaining aptness at different stages resulting in identification of proper training set with actual face image. Different datasets Yale Face Database, Extended Yale Face Database B, ORL database has been considered for our experiments. The performance of the proposed method has been evaluated on several grounds. Results show that the proposed method attains a better performance than the statistical methods.

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Vorheriges Kapitel Comparative Analysis of Different Feature Ranking Techniques in Data Mining-Based Android Malware Detection
Nächstes Kapitel Fuzzy-Based Algorithm for Resource Allocation
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Metadaten
Titel
Feature Optimality-Based Semi-supervised Face Recognition Approach
verfasst von
Taqdir
Renu dhir
Copyright-Jahr
2017
Verlag
Springer Singapore
Buch
Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications

Print ISBN: 978-981-10-3152-6

Electronic ISBN: 978-981-10-3153-3

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-981-10-3153-3_6

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