Design an Advance Computer-Aided Tool for Image Authentication and Classification
- 1 Technology and Built Environment UCSI University, Malaysia
- 2 Universiti Kebangsaan Malaysia, Malaysia
Copyright: © 2020 Rozita Teymourzadeh, Amirize Alpha Laadi, Yazan Samir Algnabi, M.D. Shabul Islam, Sawal H.M. Ali and Masuri Othman. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.
- Principal Component Analysis (PCA)
- Discrete Cosine Transform (DCT)
- Black Propagation Neural Network (BPNN)
- Local Binary Pattern (LBP)