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Lee-Ying Chong
Multimedia University
Electrical & Electronics Engineering

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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 Siew-Chin Chong