Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Principle Component Analysis

E-Passport Recognition System Based on ANNs

Lobna Anwar Mohammed; Muzhir Shaban Al-Ani; Ali Jbaeer Dawood

Engineering and Technology Journal, 2016, Volume 34, Issue 6, Pages 860-870

Nowadays it is more necessary to perform the identity check of passengers quickly and reliably to prevent unauthorized border crossing, and limit the use of forger passport. This paper concentrated on the design E-passport using two main technologies which are biometric and RFID technologies. Biometric features are used to identify passport holder and the RFID is used to store andtransmit these features as required. This paper proposes a new approach to design and implement a robust biometric recognition system that could be used in e-passport system to identify and recognize person that own the identical e-passport. The ANN is used for recognition persons in this proposed system which was able to recognize persons registered in database in rate up to 81% and the percentage of fail in recognition was 19%.

Study of Principle Component Analysis and Learning Vector Quantization Genetic Neural Networks

Arif A. Al-Qassar; Mazin Z. Othman

Engineering and Technology Journal, 2009, Volume 27, Issue 2, Pages 321-331

In this work, the Genetic Algorithm (GA) is used to improve the performance of
Learning Vector Quantization Neural Network (LVQ-NN), simulation results show that
the GA algorithm works well in pattern recognition field and it converges much faster
than conventional competitive algorithm. Signature recognition system using LVQ-NN
trained with the competitive algorithm or genetic algorithm is proposed. This scheme
utilizes invariant moments adopted for extracting feature vectors as a preprocessing of
patterns and a single layer neural network (LVQ-NN) for pattern classification. A very
good result has been achieved using GA in this system. Moreover, the Principle
Component Analysis Neural Network (PCA-NN) which its learning technique is
classified as unsupervised learning is also enhanced by hybridization with the genetic
algorithm. Three algorithms were used to train the PCA-NN. These are Generalized
Hebbian Algorithm (GHA), proposed Genetic Algorithm and proposed Hybrid
Neural/Genetic Algorithm (HNGA).