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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Hong Wei
University of Reading
Computer Science & IT Electrical & Electronics Engineering

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Investigation of robust gait recognition for different appearances and camera view angles Chirawat Wattanapanich; Hong Wei; Wijittra Petchkit
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp3977-3987

Abstract

A gait recognition framework is proposed to tackle the challenge of unknown camera view angles as well as appearance changes in gait recognition. In the framework, camera view angles are firstly identified before gait recognition. Two compact images, gait energy image (GEI) and gait modified Gaussian image (GMGI), are used as the base gait feature images. Histogram of oriented gradients (HOG) is applied to the base gait feature images to generate feature descriptors, and then a final feature map after principal component analysis (PCA) operations on the descriptors are used to train support vector machine (SVM) models for individuals. A set of experiments are conducted on CASIA gait database B to investigate how appearance changes and unknown view angles affect the gait recognition accuracy under the proposed framework. The experimental results have shown that the framework is robust in dealing with unknown camera view angles, as well as appearance changes in gait recognition. In the unknown view angle testing, the recognition accuracy matches that of identical view angle testing in gait recognition. The proposed framework is specifically applicable in personal identification by gait in a small company/organization, where unintrusive personal identification is needed.
Implementation of deep neural networks (DNN) with batch normalization for batik pattern recognition Ida Nurhaida; Vina Ayumi; Devi Fitrianah; Remmy A. M. Zen; Handrie Noprisson; Hong Wei
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (580.261 KB) | DOI: 10.11591/ijece.v10i2.pp2045-2053

Abstract

One of the most famous cultural heritages in Indonesia is batik. Batik is a specially made drawing cloth by writing Malam (wax) on the cloth, then processed in a certain way. The diversity of motifs both in Indonesia and the allied countries raises new research topics in the field of information technology, both for conservation, storage, publication and the creation of new batik motifs. In computer science research area, studies about Batik pattern have been done by researchers and some algorithms have been successfully applied in Batik pattern recognition. This study was focused on Batik motif recognition using texture fusion feature which is Gabor, Log-Gabor, and GLCM; and using PCA feature reduction to improve the classification accuracy and reduce the computational time. To improve the accuracy, we proposed a Deep Neural Network model to recognise batik pattern and used batch normalisation as a regularises to generalise the model and to reduce time complexity. From the experiments, the feature extraction, selection, and reduction gave better accuracy than the raw dataset. The feature selection and reduction also reduce time complexity. The DNN+BN significantly improve the accuracy of the classification model from 65.36% to 83.15%. BN as a regularization has successfully made the model more general, hence improve the accuracy of the model. The parameters tuning also improved accuracy from 83.15% to 85.57%.
Texture Fusion for Batik Motif Retrieval System Ida Nurhaida; Hong Wei; Remmy A. M. Zen; Ruli Manurung; Aniati M. Arymurthy
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 6: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1442.028 KB) | DOI: 10.11591/ijece.v6i6.pp3174-3187

Abstract

This paper systematically investigates the effect of image texture features on batik motif retrieval performance. The retrieval process uses a query motif image to find matching motif images in a database. In this study, feature fusion of various image texture features such as Gabor, Log-Gabor, Grey Level Co-Occurrence Matrices (GLCM), and Local Binary Pattern (LBP) features are attempted in motif image retrieval. With regards to performance evaluation, both individual features and fused feature sets are applied. Experimental results show that optimal feature fusion outperforms individual features in batik motif retrieval. Among the individual features tested, Log-Gabor features provide the best result. The proposed approach is best used in a scenario where a query image containing multiple basic motif objects is applied to a dataset in which retrieved images also contain multiple motif objects. The retrieval rate achieves 84.54% for the rank 3 precision when the feature space is fused with Gabor, GLCM and Log-Gabor features. The investigation also shows that the proposed method does not work well for a retrieval scenario where the query image contains multiple basic motif objects being applied to a dataset in which the retrieved images only contain one basic motif object.
Determining the Number of Batik Motif Object based on Hierarchical Symmetry Detection Approach Ida Nurhaida; Remmy A. M. Zen; Vina Ayumi; Hong Wei
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 1: March 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i1.2369

Abstract

In certain conditions, symmetry can be used to describe objects in the batik motif efficiently. Symmetry can be defined based on three linear transformations of dimension n in Euclidian space in the form of translation and rotation. This concept is useful for detecting objects and recognising batik motifs. In this study, we conducted a study of the symmetry effect to determine the number of batik motif objects in an image using symmetry algorithm through a hierarchical approach. The process focuses on determining the intersection line of the batik motif object. Furthermore, by utilising intersection line information for bilateral and rotational symmetry, the number of objects carried out recursively is determined. The results obtained are numbers of batik motif objects through symmetry detection. This information will be used as a reference for batik motif detection. Based on the experimental results, there are some errors caused by the axis of the symmetry line that is not appropriate due to the characteristics of batik motifs. The problem is solved by adding several rules to detect symmetry line and to determine the number of objects. The additional rules increase the average accuracy of the number of object detection from 66.21% to 86.19% (19.99% increase).
Co-Authors Aniati M. Arymurthy Ayumi, Vina Chirawat Wattanapanich Devi Fitrianah Ida Nurhaida Noprisson, Handrie Remmy A. M. Zen Remmy A. M. Zen Ruli Manurung Wijittra Petchkit