Perbandingan Performa SIFT dan ORB dalam Pengolahan Dataset Wajah nist_2
Abstract
The problem of detecting and matching facial features in digital images is becoming increasingly crucial with the development of biometrics and human computer interaction applications, especially under varying lighting, orientation, and expression conditions. In this study, two feature detection and matching algorithms Scale Invariant Feature Transform (SIFT) and Oriented FAST and Rotated BRIEF (ORB) are compared on grayscale images processed using adaptive local contrast and Gaussian filtering. The performance of both algorithms is quantitatively evaluated based on the number of keypoints, matching precision, execution time, and visual accuracy. Experimental results show that ORB has an execution time of about 4.7-fold faster than SIFT, indicating ORB’s suitability for real time applications. In contrast, SIFT produces a higher matching rate and shows better robustness to lighting variations and facial deformation. These findings provide practical guidelines for selecting algorithms based on priority: speed in real time applications or accuracy in challenging environmental conditions.
Kata kunci: Feature matching; Facial image processing; Adaptive local contrast; Scale Invariant Feature Transform; Oriented FAST and Rotated BRIEF.
Abstrak
Permasalahan deteksi dan pencocokan fitur wajah pada citra digital menjadi semakin krusial seiring berkembangnya aplikasi biometrik dan interaksi manusia komputer, terutama dalam kondisi pencahayaan, orientasi, dan ekspresi yang bervariasi. Dalam studi ini, dua algoritma deteksi dan pencocokan fitur Scale Invariant Feature Transform (SIFT) dan Oriented FAST and Rotated BRIEF (ORB) dibandingkan pada citra grayscale yang telah diproses menggunakan adaptive local contrast dan Gaussian filtering. Kinerja kedua algoritma dievaluasi secara kuantitatif berdasarkan jumlah titik kunci, presisi pencocokan, waktu eksekusi, dan akurasi visual. Hasil eksperimen menunjukkan bahwa ORB memiliki waktu eksekusi sekitar 4,7 lipat lebih cepat daripada SIFT, menandakan kecocokan ORB untuk aplikasi real time. Sebaliknya, SIFT menghasilkan tingkat kecocokan yang lebih tinggi dan menunjukkan ketahanan yang lebih baik terhadap variasi pencahayaan dan deformasi wajah. Temuan ini memberikan pedoman praktis dalam memilih algoritma sesuai prioritas: kecepatan pada aplikasi real time atau ketelitian dalam kondisi lingkungan yang menantang.
Keywords
References
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