H.6. Pattern Recognition
1. Robust Iris Recognition in Unconstrained Environments

A. Noruzi; M. Mahlouji; A. Shahidinejad

Volume 7, Issue 4 , Autumn 2019, , Pages 495-506

http://dx.doi.org/10.22044/jadm.2019.7434.1884

Abstract
  A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by him/her. Iris recognition (IR) is known to be the most reliable and accurate biometric identification system. The iris recognition system (IRS) consists of an automatic segmentation ...  Read More

H.6. Pattern Recognition
2. 2D Dimensionality Reduction Methods without Loss

S. Ahmadkhani; P. Adibi; A. ahmadkhani

Volume 7, Issue 1 , Winter 2019, , Pages 203-212

http://dx.doi.org/10.22044/jadm.2017.5063.1610

Abstract
  In this paper, several two-dimensional extensions of principal component analysis (PCA) and linear discriminant analysis (LDA) techniques has been applied in a lossless dimensionality reduction framework, for face recognition application. In this framework, the benefits of dimensionality reduction were ...  Read More

H.6. Pattern Recognition
3. A Geometry Preserving Kernel over Riemannian Manifolds

Kh. Sadatnejad; S. Shiry Ghidari; M. Rahmati

Volume 6, Issue 2 , Summer 2018, , Pages 321-334

http://dx.doi.org/10.22044/jadm.2017.1000

Abstract
  Abstract- Kernel trick and projection to tangent spaces are two choices for linearizing the data points lying on Riemannian manifolds. These approaches are used to provide the prerequisites for applying standard machine learning methods on Riemannian manifolds. Classical kernels implicitly project data ...  Read More

H.6. Pattern Recognition
4. Holistic Farsi handwritten word recognition using gradient features

Z. Imani; Z. Ahmadyfard; A. Zohrevand

Volume 4, Issue 1 , Winter 2016, , Pages 19-25

http://dx.doi.org/10.5829/idosi.JAIDM.2016.04.01.03

Abstract
  In this paper we address the issue of recognizing Farsi handwritten words. Two types of gradient features are extracted from a sliding vertical stripe which sweeps across a word image. These are directional and intensity gradient features. The feature vector extracted from each stripe is then coded using ...  Read More

H.6. Pattern Recognition
5. Classification of ECG signals using Hermite functions and MLP neural networks

A. Ebrahimzadeh; M. Ahmadi; M. Safarnejad

Volume 4, Issue 1 , Winter 2016, , Pages 55-65

http://dx.doi.org/10.5829/idosi.JAIDM.2016.04.01.07

Abstract
  Classification of heart arrhythmia is an important step in developing devices for monitoring the health of individuals. This paper proposes a three module system for classification of electrocardiogram (ECG) beats. These modules are: denoising module, feature extraction module and a classification module. ...  Read More

H.6. Pattern Recognition
6. IRDDS: Instance reduction based on Distance-based decision surface

J. Hamidzadeh

Volume 3, Issue 2 , Summer 2015, , Pages 121-130

http://dx.doi.org/10.5829/idosi.JAIDM.2015.03.02.01

Abstract
  In instance-based learning, a training set is given to a classifier for classifying new instances. In practice, not all information in the training set is useful for classifiers. Therefore, it is convenient to discard irrelevant instances from the training set. This process is known as instance reduction, ...  Read More