Implementation of Back Propagation Neural Network with PCA for Face Recognition

Md. Manik Ahmed; A F M Zainul Abadin; Md. Imran Hossain

Implementation of Back Propagation Neural Network with PCA for Face Recognition

Md. Manik Ahmed * § B.Sc. (Engineering) in ICE

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A F M Zainul Abadin

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Md. Imran Hossain

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§ Rabindra Maitree University, Kushtia, Bangladesh.

Implementation of Back Propagation Neural Network with PCA for Face Recognition

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Abstract

Face recognition is truly one of the demanding fields of biometric image processing system. Within this paper, we have implemented Back Propagation Neural Network for face recognition using MATLAB, where feature extraction and face identification system completely depend on Principal Component Analysis (PCA). Face images are multidimensional and variable data. Hence we cannot directly apply Back Propagation Neural Network to classify face without extracting the core area of face. So, the dimensionality of face image is reduced by the Principal Component Analysis algorithm then we have to explore unique feature for all stored database images called eigenfaces of eigenvectors. These unique features or eigenvectors are given as parallel input to the Back Propagation Neural Network (BPNN) for recognition of given test images. Here test image is taken from the integrated webcam which is applied to the BPNN trained network. The maximum output of the tested network gives the index of recognized face image. BPNN employing PCA is more robust and reliable than PCA based face recognition system.

References

8 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

Md. Manik Ahmed, A F M Zainul Abadin, Md. Imran Hossain. 2019. "Implementation of Back Propagation Neural Network with PCA for Face Recognition". Global Journal of Computer Science and Technology - G: Interdisciplinary GJCST-G Volume 19 (GJCST Volume 19 Issue G3).

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GJCST Volume 19 Issue G3

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

GJCST-G Classification I.2.6

Submission ReceivedDecember 8, 2018
Peer Review Double Blind
Handling Editor
Accepted January 1, 2019
Published January 15, 2019

Version of record

v1.2

Issue date

August 7, 2019

Language

English

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Article Matrices

Total Score: 144

Country: Bangladesh

Subject: Global Journal of Computer Science and Technology

Authors: Md. Manik Ahmed, A F M Zainul Abadin, Md. Anwar Hossain, Md. Imran Hossain (PhD/Dr. count: 0)

View Count (all-time): 478

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Publish Date: 2019 01, Tue

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