Design an advance computer-aided tool for image authentication and classification

Rozita Teymourzadeh, Amirize Alpha Laadi, Yazan Samir Algnabi, M. D. Shabul Islam, Sawal Hamid Md Ali, Masuri Othman

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Over the years, advancements in the fields of digital image processing and artificial intelligence have been applied in solving many real-life problems. This could be seen in facial image recognition for security systems, identity registrations. Hence a bottleneck of identity registration is image processing. These are carried out in form of image preprocessing, image region extraction by cropping, feature extraction using Principal Component Analysis (PCA) and image compression using Discrete Cosine Transform (DCT). Other processing include filtering and histogram equalization using contrast stretching is performed while enhancing the image as part of the analytical tool. Hence, this research work presents a universal integration image forgery detection analysis tool with image facial recognition using Black Propagation Neural Network (BPNN) processor. The proposed designed tool is a multi-function smart tool with the novel architecture of programmable error goal and light intensity. Furthermore, its advance dual database increases the efficiency for high performance application. With the fact that, the facial image recognition will always, give a matching output or closest possible output image for every input image irrespective of the authenticity, the universal smart GUI tool is proposed and designed to perform image forgery detection with the high accuracy of ±2% error rate. Meanwhile, a novel structure that provides efficient automatic image forgery detection for all input test images for the BPNN recognition is presented. Hence, an input image will be authenticated before being fed into the recognition tool.

Original languageEnglish
Pages (from-to)696-705
Number of pages10
JournalAmerican Journal of Applied Sciences
Volume10
Issue number7
DOIs
Publication statusPublished - 2013

Fingerprint

Authentication
Image recognition
Image processing
Neural networks
Discrete cosine transforms
Graphical user interfaces
Image compression
Security systems
Principal component analysis
Stretching
Artificial intelligence
Feature extraction
Processing

Keywords

  • Black Propagation Neural Network (BPNN)
  • Discrete Cosine Transform (DCT)
  • Local Binary Pattern (LBP)
  • Principal Component Analysis (PCA)

ASJC Scopus subject areas

  • General

Cite this

Design an advance computer-aided tool for image authentication and classification. / Teymourzadeh, Rozita; Alpha Laadi, Amirize; Samir Algnabi, Yazan; Shabul Islam, M. D.; Md Ali, Sawal Hamid; Othman, Masuri.

In: American Journal of Applied Sciences, Vol. 10, No. 7, 2013, p. 696-705.

Research output: Contribution to journalArticle

Teymourzadeh, R, Alpha Laadi, A, Samir Algnabi, Y, Shabul Islam, MD, Md Ali, SH & Othman, M 2013, 'Design an advance computer-aided tool for image authentication and classification', American Journal of Applied Sciences, vol. 10, no. 7, pp. 696-705. https://doi.org/10.3844/ajassp.2013.696.705
Teymourzadeh, Rozita ; Alpha Laadi, Amirize ; Samir Algnabi, Yazan ; Shabul Islam, M. D. ; Md Ali, Sawal Hamid ; Othman, Masuri. / Design an advance computer-aided tool for image authentication and classification. In: American Journal of Applied Sciences. 2013 ; Vol. 10, No. 7. pp. 696-705.
@article{f4b19a08dd76459ebbd12b81bf874c9c,
title = "Design an advance computer-aided tool for image authentication and classification",
abstract = "Over the years, advancements in the fields of digital image processing and artificial intelligence have been applied in solving many real-life problems. This could be seen in facial image recognition for security systems, identity registrations. Hence a bottleneck of identity registration is image processing. These are carried out in form of image preprocessing, image region extraction by cropping, feature extraction using Principal Component Analysis (PCA) and image compression using Discrete Cosine Transform (DCT). Other processing include filtering and histogram equalization using contrast stretching is performed while enhancing the image as part of the analytical tool. Hence, this research work presents a universal integration image forgery detection analysis tool with image facial recognition using Black Propagation Neural Network (BPNN) processor. The proposed designed tool is a multi-function smart tool with the novel architecture of programmable error goal and light intensity. Furthermore, its advance dual database increases the efficiency for high performance application. With the fact that, the facial image recognition will always, give a matching output or closest possible output image for every input image irrespective of the authenticity, the universal smart GUI tool is proposed and designed to perform image forgery detection with the high accuracy of ±2{\%} error rate. Meanwhile, a novel structure that provides efficient automatic image forgery detection for all input test images for the BPNN recognition is presented. Hence, an input image will be authenticated before being fed into the recognition tool.",
keywords = "Black Propagation Neural Network (BPNN), Discrete Cosine Transform (DCT), Local Binary Pattern (LBP), Principal Component Analysis (PCA)",
author = "Rozita Teymourzadeh and {Alpha Laadi}, Amirize and {Samir Algnabi}, Yazan and {Shabul Islam}, {M. D.} and {Md Ali}, {Sawal Hamid} and Masuri Othman",
year = "2013",
doi = "10.3844/ajassp.2013.696.705",
language = "English",
volume = "10",
pages = "696--705",
journal = "American Journal of Applied Sciences",
issn = "1546-9239",
publisher = "Science Publications",
number = "7",

}

TY - JOUR

T1 - Design an advance computer-aided tool for image authentication and classification

AU - Teymourzadeh, Rozita

AU - Alpha Laadi, Amirize

AU - Samir Algnabi, Yazan

AU - Shabul Islam, M. D.

AU - Md Ali, Sawal Hamid

AU - Othman, Masuri

PY - 2013

Y1 - 2013

N2 - Over the years, advancements in the fields of digital image processing and artificial intelligence have been applied in solving many real-life problems. This could be seen in facial image recognition for security systems, identity registrations. Hence a bottleneck of identity registration is image processing. These are carried out in form of image preprocessing, image region extraction by cropping, feature extraction using Principal Component Analysis (PCA) and image compression using Discrete Cosine Transform (DCT). Other processing include filtering and histogram equalization using contrast stretching is performed while enhancing the image as part of the analytical tool. Hence, this research work presents a universal integration image forgery detection analysis tool with image facial recognition using Black Propagation Neural Network (BPNN) processor. The proposed designed tool is a multi-function smart tool with the novel architecture of programmable error goal and light intensity. Furthermore, its advance dual database increases the efficiency for high performance application. With the fact that, the facial image recognition will always, give a matching output or closest possible output image for every input image irrespective of the authenticity, the universal smart GUI tool is proposed and designed to perform image forgery detection with the high accuracy of ±2% error rate. Meanwhile, a novel structure that provides efficient automatic image forgery detection for all input test images for the BPNN recognition is presented. Hence, an input image will be authenticated before being fed into the recognition tool.

AB - Over the years, advancements in the fields of digital image processing and artificial intelligence have been applied in solving many real-life problems. This could be seen in facial image recognition for security systems, identity registrations. Hence a bottleneck of identity registration is image processing. These are carried out in form of image preprocessing, image region extraction by cropping, feature extraction using Principal Component Analysis (PCA) and image compression using Discrete Cosine Transform (DCT). Other processing include filtering and histogram equalization using contrast stretching is performed while enhancing the image as part of the analytical tool. Hence, this research work presents a universal integration image forgery detection analysis tool with image facial recognition using Black Propagation Neural Network (BPNN) processor. The proposed designed tool is a multi-function smart tool with the novel architecture of programmable error goal and light intensity. Furthermore, its advance dual database increases the efficiency for high performance application. With the fact that, the facial image recognition will always, give a matching output or closest possible output image for every input image irrespective of the authenticity, the universal smart GUI tool is proposed and designed to perform image forgery detection with the high accuracy of ±2% error rate. Meanwhile, a novel structure that provides efficient automatic image forgery detection for all input test images for the BPNN recognition is presented. Hence, an input image will be authenticated before being fed into the recognition tool.

KW - Black Propagation Neural Network (BPNN)

KW - Discrete Cosine Transform (DCT)

KW - Local Binary Pattern (LBP)

KW - Principal Component Analysis (PCA)

UR - http://www.scopus.com/inward/record.url?scp=84879900618&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84879900618&partnerID=8YFLogxK

U2 - 10.3844/ajassp.2013.696.705

DO - 10.3844/ajassp.2013.696.705

M3 - Article

AN - SCOPUS:84879900618

VL - 10

SP - 696

EP - 705

JO - American Journal of Applied Sciences

JF - American Journal of Applied Sciences

SN - 1546-9239

IS - 7

ER -