Face Recognition Under Varying Illumination Using Principal Component Analysis and Local Binary Patterns

International Journal of Computer Science and Technology | Vol. 7, No. 1, January 2005 | pp. 1–8

DOI: 10.46882/2005/IJCST/070104

Research Article

Title: Face Recognition Under Varying Illumination Using Principal Component Analysis and Local Binary Patterns

Names of Authors: Sofia Alvarez¹, Jean-Pierre Dubois²

Authors’ Affiliations: ¹Departamento de Ingeniería de Sistemas, Universidad Nacional de Colombia, Bogotá, Colombia; ²Laboratoire d'Informatique, Université de Paris-Sud, Orsay, France

Abstract:
Variations in environmental lighting conditions present a persistent barrier to the high-accuracy deployment of automated facial recognition systems. This research describes a hybrid feature extraction model that merges the global dimensionality reduction capabilities of Principal Component Analysis (PCA) with the local structural robustness of Local Binary Patterns (LBP). The pre-processing pipeline employs a modified histogram equalization step to balance intensity gradients before extracting the multi-scale LBP histograms. Testing was performed using the Extended Yale Face Database B, which contains extreme illumination variances. The combined PCA-LBP approach yielded an overall verification accuracy rate of 98.64%, outpacing standalone PCA implementations by 11.3% and standard Linear Discriminant Analysis (LDA) frameworks by 6.7%. The classification phase, driven by a nearest-neighbor classifier using Chi-square distance metrics, completed profiling tasks in approximately t = 14.5 ms per image. This performance baseline makes the algorithm highly suitable for integration into live biometric surveillance workflows.

Keywords: Face Recognition, Principal Component Analysis, Local Binary Patterns, Feature Extraction, Illumination Invariance

Manuscript Timeline: Received October 15, 2004; Revised November 30, 2004; Accepted December 18, 2004; Published January 20, 2005

Citation: Alvarez, S., & Dubois, J. P. (2005). Face Recognition Under Varying Illumination Using Principal Component Analysis and Local Binary Patterns. International Journal of Computer Science and Technology, 7(1), 1–8.

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