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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Aruming Tyas, Dyah
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Computer Science & IT Engineering

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Modified zero-reference deep curve estimation for contrast quality enhancement in face recognition Kahfi Aulia, Muhammad; Aruming Tyas, Dyah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3274-3286

Abstract

Face recognition systems remain challenged by variable lighting conditions. While zero-reference deep curve estimation (Zero-DCE) effectively enhances low-light images, it frequently induces overexposure in normal- and high-brightness scenarios. This study introduces modified Zero-DCE combined with three established enhancement techniques: contrast stretching (CS), contrast limited adaptive histogram equalization (CLAHE), and brightness preserving dynamic histogram equalization (BPDHE). Evaluations employed the extended Yale face database B and face recognition technology (FERET) datasets, with 10 representative samples assessed using the blind/referenceless image spatial quality evaluator (BRISQUE) metric. Modified Zero-DCE with BPDHE produced optimal enhancement quality, achieving a mean BRISQUE score of 16.018. On the extended Yale face database B, visual geometry group 16 (VGG16) integrated with modified Zero-DCE and CLAHE attained 83.65% recognition accuracy, representing a 6.08-percentage-point improvement over conventional Zero-DCE. For the 200-subject FERET subset, residual network 50 (ResNet50) with modified Zero-DCE and CLAHE achieved 67.41% accuracy. Notably, standard Zero-DCE with CLAHE demonstrated superior robustness in extremely low-light conditions, highlighting the illumination-dependent performance characteristics of these enhancement approaches.
Co-Authors Kahfi Aulia, Muhammad