Peningkatan Performa Pengenalan Wajah pada Gambar Low-Resolution Menggunakan Metode Super-Resolution
DOI:
https://doi.org/10.25126/jtiik.20241117947Kata Kunci:
downsampling, low-resolution, super-resolution, pengenalan wajah, gambar potret wajahAbstrak
Kartu Tanda Penduduk Elektronik (KTP-el) merupakan identitas wajib bagi penduduk Indonesia. Penyimpanan pada cip KTP-el yang mana selain digunakan untuk menyimpan gambar potret wajah individu, juga harus dapat menyimpan identitas lain seperti biodata, tanda tangan, dan sidik jari kiri dan kanan. Keterbatasan tersebut mengharuskan gambar potret wajah disimpan pada ukuran low-resolution (LR) sehingga sistem pengenalan wajah tidak optimal. Dalam penelitian ini, kami menggunakan Poznan University of Technology (PUT) Face database yang terdiri atas 200 gambar dari 100 individu. Data tersebut dilakukan proses down sampling menggunakan bicubic interpolation untuk menghasilkan data LR. Kami menginvestigasi penggunaan metode super-resolution (SR) berbasis deep learning, termasuk DFDNet, LapSRN, GFPGAN, Real-ESRGAN, Real-ESRGAN+GFPGAN, dan FaceSPARNet. Hal ini bertujuan untuk meningkatkan kualitas gambar LR. Evaluasi performa dilakukan dengan menggunakan matriks False Rejection Rate(FRR) pada beberapa tingkatan False Acceptance Rate (FAR). Hasil penelitian menunjukkan bahwa beberapa metode SR terutama FaceSPARNet menunjukkan peningkatan performa face recognition hingga 2%. Sedangkan, metode SR yang berbasis GAN (GFPGAN, Real-ESRGAN, Real-ESRGAN+GFPGAN) cenderung meningkatkan false reject rate. Penelitian ini menunjukkan bahwa metode SR dari kategori General Basic CNN-based FSR dapat digunakan untuk meningkatkan kinerja face recognition pada gambar LR, seperti pada KTP-el.
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