Studi Efek Ekualisasi Histogram dan CLAHE dalam Mendeteksi Fitur Wajah Manusia
DOI:
https://doi.org/10.22441/jte.2024.v15i2.002Kata Kunci:
biometrik, CLAHE, deteksi wajah, ekualisasi histogram, fitur wajahAbstrak
Biometrik adalah metode yang digunakan untuk mengenali identitas seseorang berdasarkan morfologi, perilaku dan organic karakteristik seperti sidik jari, iris, wajah, retina, telapak tangan dan geometri tangan, dll. Biometrik wajah mengacu pada bidang teknologi biometrik yang berfokus pada analisis dan pengenalan wajah individu untuk tujuan identifikasi dan autentikasi. Tahap pra-proses memainkan peran penting dalam sistem pendeteksi fitur wajah karena melibatkan beberapa langkah untuk meningkatkan kualitas dan keandalan citra wajah sebelum dianalisis dan dibandingkan. Input dari sistem adalah gambar wajah yang berasalah dari dataset EYB. Fitur pada wajah yang ingin dideteksi adalah mata kiri, mata kanan, hidung, dan mulut. Metode pra -proses yang diselidiki pada penelitian ini adalah histogram equalization dan Contrast Limited Adaptive Histogram Equalization (CLAHE). Hasil dari penelitian ini memperlihatkan bahwa metode pra-proses CLAHE memberikan hasil yang lebih baik dibandingkan dengan histogram equalization dalam mendeteksi fitur pada wajah.
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Referensi
W. Ouarda, H. Trichili, A. M. Alimi and B. Solaiman, "Face recognition based on geometric features using Support Vector Machines," 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR), 2014, pp. 89-95, doi: 10.1109/SOCPAR.2014.7007987.
S. Guennouni, A. Mansouri, and A. Ahaitouf, Biometric Systems and Their Applications. Visual Impairment and Blindness - What We Know and What We Have to Know,” 2019 https://doi.org/10.5772/INTECHOPEN.84845.
G. Palestra, et al. "Improved performance in facial expression recognition using 32 geometric features." International Conference on Image Analysis and Processing. Springer, Cham, 2015.
A. Aryal, and B. Becerik-Gerber, “Skin Temperature Extraction Using Facial Landmark Detection and Thermal Imaging for Comfort Assessment,” Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. https://doi.org/10.1145/3360322.3360848.
P. Terhorst, J. Kolf, M. Huber, F. Kirchbuchner, N. Damer, A. Morales, J. Fierrez, and A. Kuijper, “A Comprehensive Study on Face Recognition Biases Beyond Demographics.” IEEE Transactions on Technology and Society, 3, 16-30. 2021. https://doi.org/10.1109/tts.2021.3111823.
P. Karczmarek, W. Pedrycz, A. Kiersztyn, A. P. Rutka, “A study in facial features saliency in face recognition: an analytic hierarchy process approach,” Soft Comput. 2017, 21, 7503–7517.
M. Chaudhari, M. Deshmukh, G. Ramrakhiani, and R. Parvatikar, “Face Detection Using Viola Jones Algorithm and Neural Networks.” 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), 1-6. 2018. https://doi.org/10.1109/ICCUBEA.2018.8697768.
P. Tome, J. Fierrez, R. Vera-Rodriguez, J. Ortega-Garcia, “Combination of Face Regions in Forensic Scenarios,” J. Forensic Sci. 60, 1046–1051. 2015.
Y. Omer, R. Sapir, Y. Hatuka, and G. Yovel, “What Is a Face? Critical Features for Face Detection,” Perception, 48, 437 - 446. 2019. https://doi.org/10.1177/0301006619838734.
H. Shah, A. Dinesh, and T. Sharmila, “Analysis of Facial Landmark Features to determine the best subset for finding Face Orientation,” 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), 1-4. https://doi.org/10.1109/ICCIDS.2019.8862093.
V. M, S. Rajkumar, M. V. . Kalyan Reddy, and V. . Janesh, “Detection of Post COVID-Pneumonia Using Histogram Equalization, CLAHE Deep Learning Techniques”, ia, vol. 26, no. 72, pp. 137–145, Sep. 2023.
F.M. Hana, I.D. Maulida, “Analysis of contrast limited adaptive histogram equalization (CLAHE) parameters on finger knuckle print identification,” InJournal of Physics: Conference Series 2021 Feb 1 (Vol. 1764, No. 1, p. 012049). IOP Publishing.
A.S. Georghiades, P. N. Belhumeur and D. J. Kriegman, "From few to many: illumination cone models for face recognition under variable lighting and pose," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 643-660, June 2001, doi: 10.1109/34.927464.
K. C. Lee, J. Ho, and D.J. Kriegman, “Acquiring linear subspaces for face recognition under variable lighting,” IEEE Transactions on pattern analysis and machine intelligence, 27(5), 684-698. 2005.
P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, Kauai, HI, USA, 2001, pp. I-I, doi: 10.1109/CVPR.2001.990517.
K. Zuiderveld, “Contrast limited adaptive histogram equalization”. Graphics gems, 474-485. 1994
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