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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Journal of Computer System and Informatics (JoSYC)
Dameethia Angeline
Universitas Tarumanagara, Jakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Journal : JURNAL MEDIA INFORMATIKA BUDIDARMA

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Performa Metode Convolutional Neural Network Pada Face Landmark Untuk Virtual Make Up Try On Dameethia Angeline; Erico Jochsen; Dyah Erny Herwindiati; Janson Hendryli
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6619

Abstract

Make up or facial makeup, is an activity to change the appearance from its original form with the help of make up materials and tools. Make-up tools are beauty tools that are commonly used by most women to beautify the appearance of their faces with many shade choices. The shade on the make-up tool is the color usually used in make-up. Examples of make-up tools that are most often used include eyeshadow, blush on, and lipstick. These make-up tools are sold widely online and offline in physical stores. However, usually a tester is also needed so that those who want to buy can try the shade that suits them. When buying online, they often find it difficult to choose the right shade, while testers in physical stores are sometimes considered less hygienic because they have been used by many people. The aim of this paper is to measure the performance of the Convolutional Neural Network (CNN) method using the ResNet-50 architecture on facial landmarks for creating virtual make up try ons which can be an alternative to this problem. The facial image data source used is from the Kaggle site called Facial Keypoints Detection. The testing process produces 78.99% accuracy while the training process produces 95.12% accuracy. The evaluation results of this model use Root Mean Squared Error (RMSE) of 2.2577 and Mean Absolute Error (MAE) of 1.5389.
Co-Authors Dyah Erny Herwindiati Erico Jochsen Janson Hendryli