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Abstract
Facial Wrinkles are one of the key indicators in identifying signs of aging on the skin. Detecting facial wrinkles poses a challenge in image processing due to their complex, coarse texture, which is often difficult for computers to recognize, especially under varying lighting conditions, camera angles, and facial expressions. This study focuses on the application of features using Gabor Filters for the texture feature extraction, with the final results determined by the Naïve Bayes classification algoritm. In this study, 200 facial images were used, divided into two clases, with 100 images per class serving as training data. For the test data, 100 facial images were used, consisting of 50 wrinkled facial images and 50 non-wrinkled facial images. Based on the test result using the Confusion Matrix, the accuracy was 74%, precision 80%, recall 64% and F1-Score 71%. These results indicate that the combination of Gabor filters and Naïve Bayes is quite effective in recognizing wrinkle patterns on the face based on extracted texture feature, and can serve as a faoundation for developing more accurate facial wrinkle detection systems in the future.
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References
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References
Batool, N. And Chellappa, R. (2015) ‘Fast Detection Of Facial Wrinkles Based On Gabor Features Using Image Morphology And Geometric Constraints’, Pattern Recognition,48(3), Pp. 642–658. AvailableAt:Https://Doi.Org/10.1016/J. Patcog.2014.0 8.003.
Griffith, H.K. (2020) ‘Texture Feature Extraction From Free-Viewing Scan Paths Using Gabor Filters With Downsampling’.
Indransyah, R. Et Al. (2022) ‘Klasifikasi Sentimen Pergelaran Motogp Di Indonesia Menggunakan Algoritma Correlated Naive Bayes Clasifier’, Infotech Journal, 8(2), Pp. 60–66.
Muhimmah, I., Abriyani, F. And Kurniawardhani, A. (2018) ‘Deteksi Keriput Pada Citra Wajah Dengan Teknik Pengolahan Citra’, Pp. 1–8.
Muzaffar, A.W. Et Al. (2023) ‘Gabor Contrast Patterns : A Novel Framework To Extract Features From Texture Images’, IEEE Access, 11(May), Pp. 60324– 60334. Available At: Https://Doi.Org/10.1109/ACCESS.202 3. 3280053.
Nasional, S. Et Al. (2024) ‘Implementasi Metode Filter Gabor Pada Ekstraksi Fitur Image Wajah’, Pp. 325–330.
Putra, D.R.H., Marisa, F. And Wijaya, I.D. (2018) ‘Identifikasi Wajah Berbasis Segmentasi Warna Kulit Wajah Menggunakan Naive Bayes Classifier’, Jurnal Teknologi Informasi, 9, Pp. 99– 106. Available At: Https://Doi.Org/10.36382/Jtitki.V9i2.3 29.
Sheikh, M.A.A. Of E. And C.E.A.U.) (2022) ‘Extraction Of Man-Made Object From Remote Sensing Images Using Gabor’, 2.
Suatkab, S.G. And (Stevie), A.S. (2021) ‘Aplikasi Pengekstrak Ciri Keriput Wajah Manusia Dengan Pendekatan Keacakkan Statistis Entropi’, Indonesian Journal Of Technology, Informatics And Science (IJTIS), 2(2), Pp. 85–92. Available At: Https://Doi.Org/10.24176/Ijtis.V2i2.63 02.
Sulistiyanti, S. Ratna., Setyawan, F.A. And Komarudin, M. (2016) Pengolahan Citra Dasar Dan Contoh Penerapannya. Edisi Pert. Edited ByWarsito. Yogyakarta: Teknosain.
Vazquez-Fernandez, E. Et Al. (2010) ‘Entropy Of Gabor Filtering For Image Quality Assessment’, Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 6111 LNCS(PART 1), Pp. 52–61. Available At: Https://Doi.Org/10.1007/978-3- 642- 13772-3_6.
Wahyuningtyas, R.S. Et Al. (2015) ‘Sistem Pakar Penentuan Jenis Kulit Wajah Wanita Menggunakan Metode Naïve Bayes’, 1(1).