Face Recognition using Fused Diagonal and Matrix Features

Face Recognition using Fused Diagonal and Matrix Features

Jagadeesh H S

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Suresh Babu K

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K B Raja

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α Visvesvaraya Technological University

Face Recognition using Fused Diagonal and Matrix Features

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Abstract

Face recognition with less information availability in terms of the number of image samples is a challenging task. A simple and efficient method for face recognition is proposed in this paper, to address small sample size problem and rotation variation of input images. The robert`s operator is used as edge detection method to elicit borders to crop the facial part and then all cropped images are resized to a uniform 50*50 size to complete the preprocessing step. Preprocessed test images are rotated in different angles to check the robustness of proposed algorithm. All preprocessed images are partitioned into one hundred 5*5 equal size parts. The matrix 2-norm, infinite norm, trace and rank are elicited for each of 5*5 part and respectively averaged to yield on hundred matrix features. Another one hundred diagonal features are extracted by applying a 3*3 mask on each image. Final one hundred features are obtained by fusing averaged matrix and diogonal features. Euclidian distance measure is used for comparision of database and query image features. The results are comparitively better on three publically availabe datasets compared to existing methods.

References

23 Cites in Article

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Jagadeesh H S. 2016. \u201cFace Recognition using Fused Diagonal and Matrix Features\u201d. Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 16 (GJCST Volume 16 Issue F1): .

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GJCST Volume 16 Issue F1
Pg. 17- 27

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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Classification

GJCST-F Classification: I.4.8 I.7.5

Submission ReceivedDecember 9, 2015
Peer Review Double Blind
Handling Editor
Accepted January 3, 2016
Published January 15, 2016

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v1.2

Issue date

April 20, 2016

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Article Matrices

Total Score: 103

Country: India

Subject: Global Journal of Computer Science and Technology - F: Graphics & Vision

Authors: Jagadeesh H S, Suresh Babu K, K B Raja (PhD/Dr. count: 0)

View Count (all-time): 260

Total Views (Real + Logic): 7571

Total Downloads (simulated): 1884

Publish Date: 2016 04, Wed

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Total Views: 7571

Total Downloads: 1884

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