Year: 2025
Type: article
Abstract: Facial recognition systems often struggle under extreme lighting conditions, which distort facial features and reduce recognition accuracy. This study introduces a novel integration of DeepFace embeddings with a lightweight Multi-Layer Perceptron (MLP) classifier tailored to improve facial recognition under extreme lighting conditions. This combination has not been explored in previous studies and offers a compact alternative to conventional CNN-based methods. The Labeled Faces in the Wild (LFW) dataset was augmented using rotation, flipping, and lighting variations, and further enhanced with CLAHE for improved contrast under poor illumination. The resulting 128-dimensional DeepFace embeddings were classified using a four-layer MLP with LeakyReLU activation, Batch Normalization, and Dropout. The model was evaluated across three data-splitting schemes (70:30, 80:20, and 90:10), with the 80:20 configuration achieving the highest accuracy of 95.16%. Compared to the baseline CNN, the proposed method demonstrated superior robustness to illumination variations. This makes the proposed model suitable for real-time applications such as biometric authentication and AI-based surveillance systems.
Authors: Dede Kurniadi, Erick Fernando, Asyifa Fauziyah, Asri Mulyani
Institutions: Institut Teknologi Garut, Multimedia Nusantara University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $150
Funder: Universitas Multimedia Nusantara
OpenAlex ID: W4416525797
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