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Erschienen in:
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2021 | OriginalPaper | Buchkapitel

Optimized Dynamic Feature Matching for Face Recognition

verfasst von : Ganesh Gopalrao Patil, Rohitash Kumar Banyal

Erschienen in: Techno-Societal 2020

Verlag: Springer International Publishing

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Abstract

Since the last three decades, face detection and recognition have become very active and a huge part of image processing research. In real-time applications like video surveillance, front views cannot be guaranteed as input. Hence the failure rates can degrade the performance of the face recognition system. The proposal aims to introduce a novel PFR method termed as DFM that combine Sparse Representation Classification (SRC) and FCN for resolving the partial face recognition issues. As the major contribution, this proposal aims to tune the sparse coefficient of DFM in an optimal manner, such that the reconstruction error should be minimal. Moreover, this proposal introduces Jaccard Similarity Index measure to calculate the similarity scores among the gallery sub feature map and probe feature map. For optimization purpose, this work deploys a hybrid algorithm that hybrids both the concepts of Grey Wolf Optimization (GWO) and Sea Lion Optimization (SLnO) algorithm.

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Metadaten
Titel
Optimized Dynamic Feature Matching for Face Recognition
verfasst von
Ganesh Gopalrao Patil
Rohitash Kumar Banyal
Copyright-Jahr
2021
Buch
Techno-Societal 2020

Print ISBN: 978-3-030-69920-8

Electronic ISBN: 978-3-030-69921-5

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-69921-5_39

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