A hybrid SUGWO optimization for partial face recognition with new similarity index

This paper introduces a Partial Face Recognition (PFR) method with the benefits of optimization logic using an optimized feature matching aspect. Besides, for better recognition, the Sparse Representation Classification (SRC) and Fully Convolutional Network (FCN) have been combined. As a novelty, th...

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Bibliographic Details
Published in:Multimedia tools and applications 2023-05, Vol.82 (12), p.18097-18116
Main Authors: Patil, Ganesh Gopalrao, Banyal, Rohitash Kumar
Format: Article
Language:English
Subjects:
Algorithms
Classification
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Discriminant analysis
Face recognition
Facial recognition technology
Feature maps
Literature reviews
Machine learning
Matching
Multimedia
Multimedia Information Systems
Optimization
Optimization algorithms
Similarity
Special Purpose and Application-Based Systems
Support vector machines
Surveillance
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Summary:This paper introduces a Partial Face Recognition (PFR) method with the benefits of optimization logic using an optimized feature matching aspect. Besides, for better recognition, the Sparse Representation Classification (SRC) and Fully Convolutional Network (FCN) have been combined. As a novelty, this work aims to tune the sparse coefficient of Dynamic Feature Matching (DFM) optimally, in which the reconstruction error should be minimal. Also, this work presents the structural similarity index measure to calculate the similarity scores between the gallery sub-feature map and probe feature map. For optimization purposes, this work deploys a proposed Sealion Updated Grey Wolf Optimization (SUGWO) algorithm. Finally, the proposed method is executed over the traditional methods concerning certain measures.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-022-14205-z