Image Preprocessing for Face Recognition

Enoch Ansumanah Stephen

doi:10.54097/8me7d488

Authors

Enoch Ansumanah Stephen

DOI:

https://doi.org/10.54097/8me7d488

Keywords:

Preprocessing, Median filtering, Difference of Gaussian Filtering (DOG), Image Processing, Gamma Correction, Contrast equalization, Illumination, Grayscale

Abstract

Face recognition is a key area in biometrics with broad use in access control, e-commerce, justice, and surveillance. A standard pipeline includes preprocessing, feature extraction, and classification; this paper concentrates on preprocessing. Using MATLAB, I implement histogram equalization, mean/median filtering, smoothing, sharpening, thresholding, edge detection, and homomorphic filtering for illumination compensation. I perform face alignment (rotation, cropping, scaling) and apply geometric and gray-level normalization to produce standardized inputs. Face regions are localized via anatomical landmarks and mathematical morphology. Simulation experiments compare configurations and show that high-quality preprocessing especially illumination normalization, denoising, and alignment improves visual clarity, stabilizes input variation, and boosts downstream localization and recognition accuracy. Overall, a well-designed preprocessing module is essential for robust face recognition in diverse conditions.

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