Performance Assessment of Principal Component Analysis and Kernel Principal Component Analysis Using TOAM Database
Madandola, Tajudeen Niyi, Gbolagade, Kazeem Alagbe, Yusuf-Asaju Ayisat Wuraola
Asian Journal of Research in Computer Science · pp. 1–10 · Published 4 Jun 2019
10.9734/ajrcos/2019/v3i230091Abstract
Face recognition algorithms can be classified into appearance-based (Linear and Non-Linear Appearance-based) and Model-based Algorithms. Principal Component Analysis (PCA) is an example of Linear Appearance-based which performs a linear dimension reduction while Kernel Principal Component Analysis (KPCA) is an example of non-linear appearance methods. The study focuses on the performance assessment of PCA and KPCA face recognition techniques. The assessment is carried out base on computational time using testing time and recognition accuracy on created database identified as TOAM database. The created database is mainly for this research purpose and it contains 120 face images of 40 persons frontal faces with 3 images of each individual under different lighting, facial expressions, occulations, environment and time. The findings reveal an average testing Time of 1.5475 seconds for PCA and 67.0929 seconds for KPCA indicating a longer Computational time for KPCA than PCA. It also reveals that PCA has 72.5% performance recognition accuracy while KPCA has 80.0% performance recognition accuracy indicating that KPCA outperforms the PCA in terms of recognition accuracy.
References (6)
- 1 Eigenfaces for Recognition [DOI]
- 2 Recognizing faces with PCA and ICA [DOI]
- 3 Introduction to Multibiometrics [DOI]
- 4 Pairwise face recognition [DOI]
- 5 Performance Evaluation Of Selected Principal Component Analysis-Based Techniques For Face Image Recognition
- 6 Performance Evaluation of Optimised PCA and Projection Combined PCA methods in Facial Images
Cited by 2
Shijing Wang, Juan Yang · Journal of Circuits Systems and Computers · 2026
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