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Journal : Teknika

Enhancing Image Quality in Facial Recognition Systems with GAN-Based Reconstruction Techniques Wijaya, Beni; Satyawan, Arief Suryadi; Haqiqi, Mokh. Mirza Etnisa; Susilawati, Helfy; Artemysia, Khaulyca Arva; Sopian, Sani Moch.; Shamie, M. Ikbal; Firman
Teknika Vol. 14 No. 1 (2025): March 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i1.1180

Abstract

Facial recognition systems are pivotal in modern applications such as security, healthcare, and public services, where accurate identification is crucial. However, environmental factors, transmission errors, or deliberate obfuscations often degrade facial image quality, leading to misidentification and service disruptions. This study employs Generative Adversarial Networks (GANs) to address these challenges by reconstructing corrupted or occluded facial images with high fidelity. The proposed methodology integrates advanced GAN architectures, multi-scale feature extraction, and contextual loss functions to enhance reconstruction quality. Six experimental modifications to the GAN model were implemented, incorporating additional residual blocks, enhanced loss functions combining adversarial, perceptual, and reconstruction losses, and skip connections for improved spatial consistency. Extensive testing was conducted using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) to quantify reconstruction quality, alongside face detection validation using SFace. The final model achieved an average PSNR of 26.93 and an average SSIM of 0.90, with confidence levels exceeding 0.55 in face detection tests, demonstrating its ability to preserve identity and structural integrity under challenging conditions, including occlusion and noise.  The results highlight that advanced GAN-based methods effectively restore degraded facial images, ensuring accurate face detection and robust identity preservation. This research provides a significant contribution to facial image processing, offering practical solutions for applications requiring high-quality image reconstruction and reliable facial recognition.
Co-Authors ADAWIYAH, AULIA Adi, Puput Dani Prasetyo Adiprabowo, Tjahjo Agus Subekti Akbar, Fabian AKBAR, FAJAR RAHMAT Ali, Abdul Latif Aloysius Adya Pramudita Aptadarya, Harwin Arentaka, Fiendo Mahendra Argaloka, Aditya Adni Ariffin, Denden Mohamad Arifyandy, Rachmat Artemysia, Khaulyca Arva Arumjeni Mitayani Aurelia, Felicia Bunga Awalya, Silmi Christian, Yohanes Wahyu Fauzan, R. Aldam Dwi Fazri, Nurul Fiky Y. Suratman Firman Galura Muhammad Suranegara Hamdani, Nizar Alam HAQIQI, MOKH MIRZA ETNISA Haqiqi, Mokh. Mirza Etnisa Harahap, Taufiq Hidayat Helfy Susilawati Hidayat, Haryanto Iswarawati, Ni Kadek Emy Jody H, Amadeus Evan KHOLIQ, ANDIKA MUHAMMAD NUR Kitagawa, Akio Laksono, Muhammad Fauzan Anggi Fathul Latukolan, Merlyn Inova Christie Linggi, Rinda Safana Manullang, Yan Ario Eko Panca Marchellyn, Ferryn Marta Dinata, Mochamad Mardi Muhammad Yassir Mulyana, Tri Munawir Munawir Nasrullah Armi Noviely, Isra Fanliv Nugroho, Agung Nurdiana, Dian NURROHMAH, IASYA FAIQOH Nurul P., Vethrea D. Gynandra Pangemanan, Agnes Novi Anna Paramita, I Gusti Ayu Putri Surya Prameswari, Aulia Widya Praptawilaga, Muhamad Fadly Rizqy Purwoko Adhi Puspita, Heni Putra, Muhammad Taufik Dwi Putra, Nyoman Triyoga Arika Putri, Riza Ayu RR. Ella Evrita Hestiandari Saharuna, Saharuna Samie, Muhammad Ikbal Saputra, Adhitya Dwi Septiyanti, Riska Yucha Shamie, M. Ikbal Siburian, Sebastian Edward Siswanti, Ike Yuni Siti Helmyati Sopian, Sani Moch Sopian, Sani Moch. Sri Desy Siswanti SUGIAN, RENDI TRI Suratman , Fiky Y. Sutejo, Muhammad Fajar Utomo, Prio Adjie Wiwik Handayani WULANDARI, ESTI FITRIA Wulandari, Ike Yuni Yuniorrita, Seszy