Digital makeup using machine learning algorithms
In this report, we present a pipeline system of digital makeup for industry scenarios. The pipeline contains two parts: i) facial feature semantic segmentation; ii) colour transfer. For facial feature semantic segmentation task, we adopt fully convolutional network (FCN) with weighted cross entropy...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| التنسيق: | Final Year Project |
| اللغة: | English |
| منشور في: |
Nanyang Technological University
2022
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/10356/156504 |
| الوسوم: |
إضافة وسم
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| المؤسسة: | Nanyang Technological University |
| اللغة: | English |
| الملخص: | In this report, we present a pipeline system of digital makeup for industry scenarios. The pipeline contains two parts: i) facial feature semantic segmentation; ii) colour transfer. For facial feature semantic segmentation task, we adopt fully convolutional network (FCN) with weighted cross entropy as loss function during training; for colour transfer task, we experimented N-dimensional Probability Density Function transfer algorithm, a fast exemplar-based image colourisation approach using colour embeddings named Color2Embed, and deep exemplar-bases colourisation approach. Considering economical and qualitative factors, we conclude that model trained by VGG16 FCN with weighted cross entropy together with fast exemplar-based image colourisation yields the most suitable result. |
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