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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Siew-Chin Chong
Multimedia University
Computer Science & IT Electrical & Electronics Engineering

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Three-dimensional shape generation via variational autoencoder generative adversarial network with signed distance function Ebenezer Akinyemi Ajayi; Kian Ming Lim; Siew-Chin Chong; Chin Poo Lee
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4009-4019

Abstract

Mesh-based 3-dimensional (3D) shape generation from a 2-dimensional (2D) image using a convolution neural network (CNN) framework is an open problem in the computer graphics and vision domains. Most existing CNN-based frameworks lack robust algorithms that can scale well without combining different shape parts. Also, most CNN-based algorithms lack suitable 3D data representations that can fit into CNN without modification(s) to produce high-quality 3D shapes. This paper presents an approach that integrates a variational autoencoder (VAE) and a generative adversarial network (GAN) called 3 dimensional variational autoencoder signed distance function generative adversarial network (3D-VAE-SDFGAN) to create a 3D shape from a 2D image that considerably improves scalability and visual quality. The proposed method only feeds a single 2D image into the network to produce a mesh-based 3D shape. The network encodes a 2D image of the 3D object into the latent representations, and implicit surface representations of 3D objects corresponding to those 2D images are subsequently generated. Hence, a signed distance function (SDF) is proposed to maintain object inside-outside information in the implicit surface representation. Polygon mesh surfaces are then produced using the marching cubes algorithm. The ShapeNet dataset was used in the experiments to evaluate the proposed 3D-VAE-SDFGAN. The experimental results show that 3D-VAE-SDFGAN outperforms other state-of-the-art models.
Kernel PCA-enhanced BoVW representation for SIFT-based face recognition using SM Chen-Han Chong; Siew-Chin Chong; Lee-Ying Chong
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10946

Abstract

This study proposes a face recognition pipeline that integrates scale-invariant feature transform (SIFT) descriptors, the bag of visual words (BoVW) model, Kernel principal component analysis (KPCA), and support vector machine (SVM) classification. It starts by extracting local keypoint descriptors from preprocessed face images using SIFT. These descriptors are subsequently vector-quantized into a visual vocabulary through MiniBatch K-Means clustering, yielding fixed-length BoVW histograms for each image. Nonlinear dimensionality reduction is achieved by applying KPCA with a radial basis function, addressing the complexity of the feature space. The resulting compact feature representations are subsequently classified using a linear SVM. The proposed method is evaluated on labelled faces in the wild (LFW) dataset with filtered 100 classes, demonstrating notable classification accuracy and reliable generalization across training, validation, and testing splits. Our experimental evaluation confirms that integrating local invariant features, nonlinear feature reduction, and discriminative classification allows the proposed method to exceed state-of-the-art face recognition performance. In addition, this proposed method is particularly suitable for scenarios with limited training data and computational resources, providing a lightweight but robust alternative to deep learning-based models.
Co-Authors Chen-Han Chong Chin Poo Lee Ebenezer Akinyemi Ajayi Kian Ming Lim Lee-Ying Chong