Classification of Skin Color Detection System to Determine Color Selection in Cosmetics (Foundation) Using Modified Chamfer Matching Algorithm Method
DOI:
https://doi.org/10.35870/jtik.v9i3.3802Keywords:
Detection, Skin Color, CMAAbstract
Skin color detectionis one of the segmentation processes that separates object regions in an image based on color differences. Objects that have a certain color are separated from objects that have other colors. The segmentation results can be used for further processes such as feature extraction or image classification. In this example, skin color is defined in the YCbCr color space with a Cb value between 77 and 127 and a Cr value between 133 and 173. Skin color detection is one of the initial stages in computer vision to detect things related to humans (people detection). Skin color detection can be used as a segmentation method for face recognition or recognition of other body organs. The system can be further developed for biometric systems.
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Aziz, A., & Karpen, K. (2019). Diagnosa Penyakit Kulit Wajah Menggunakan Metode Decession Tree Dan Algoritma C4. 5. Jurnal Teknologi Dan Open Source, 2(1), 74-86. https://doi.org/10.36378/jtos.v2i1.148.
Cahyaningsih, S., Triayudi, A., & Sholihati, I. D. (2021). Kombinasi metode certainty factor dan forward chaining untuk identifikasi jenis kulit wajah berbasis Android. Jurnal Media Informatika Budidarma, 5(1), 74. https://doi.org/10.30865/Mib.V5i1.2591.
Daniati, E., & Nugroho Skom, A. (2017). Aplikasi perawatan wajah berdasarkan jenis kulit wajah. Jurnal Simki-Techsain, 1, 12.
Firman, A., Wowor, H. F., & Najoan, X. (2016). Sistem informasi perpustakaan online berbasis web. Jurnal Teknik Elektro dan Komputer, 5(2), 29-36.
Gede Astuti Gede Aditya Mahardika Pratama, L. (2021). The expert system design to identify laptop damage by applying certainty factor method. International Research Journal of Engineering, IT & Scientific Research, 7(4), 165–175. https://doi.org/10.21744/Irjeis.V7n4.1898.
Hamrani, A., Leizaola, D., Reddy Vedere, N. K., Kirsner, R. S., Kaile, K., Trinidad, A. L., & Godavarty, A. (2024). AI Dermatochroma Analytica (AIDA): Smart Technology for Robust Skin Color Classification and Segmentation. Cosmetics, 11(6), 218.
Hsiao, S. W., Yen, C. H., & Lee, C. H. (2017). An intelligent skin‐color capture method based on fuzzy C‐means with applications. Color Research & Application, 42(6), 775-787.
Jufri, M. (2022). Designing an Expert System for Diagnosing Otitis Disease Using Forward Chaining and Certainty Factor Methods. IJISTECH (International Journal of Information System and Technology), 6(2), 282-289. https://doi.org/10.30645/ijistech.v6i2.240.
Kakumanu, P., Makrogiannis, S., & Bourbakis, N. (2007). A survey of skin-color modeling and detection methods. Pattern recognition, 40(3), 1106-1122. https://doi.org/10.1016/j.patcog.2006.06.010.
Krismawati, Y. (2021). EXPERT SYSTEM FOR DETECTION OF SKIN ON THE FACE IN THE D'ANGELS BEAUTY CLINIC. International Journal of Artificial Intelligence and Robotic Technology (IJAIRTec), 1(1), 17-21.
Kumarahadi, Y. K., Arifin, M. Z., Pambudi, S., Prabowo, T., & Kusrini, K. (2020). Sistem pakar identifikasi jenis kulit wajah dengan metode certainty factor. Jurnal Teknologi Informasi dan Komunikasi (Tikomsin), 8(1).
Pebrianto, R., Nugraha, S. N., & Gata, W. (2020). Perancangan Sistem Pakar Penentuan Jenis Kulit Wajah Menggunakan Metode Certainty Factor. IJCIT (Indonesian Journal on Computer and Information Technology), 5(1), 83-93.
Sitinjak, D. D. J. T., & Suwita, J. (2020). Analisa Dan Perancangan Sistem Informasi Administrasi Kursus Bahasa Inggris Pada Intensive English Course Di Ciledug Tangerang. Insan Pembangunan Sistem Informasi dan Komputer (IPSIKOM), 8(1). https://doi.org/10.58217/ipsikom.v8i1.164.
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