基于深度学习的化妆人脸验证网络

Jiawei Hou, Zhaohui Wang, Yigan Li
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引用次数: 3

摘要

化妆,源于人类对美的追求,它改变了人们的外貌形象,带来了更多美的享受和精神上的愉悦。然而,最近的研究表明,面部化妆对面部识别有负面影响。为了解决这个问题,我们构建了一个端到端的深度学习网络,该网络由一个主干CNN和一个新颖的映射模块组成。具体来说,我们在一个全面的数据集上预训练我们的框架,并在化妆数据集上微调我们的映射模块。然后在这些数据集上进行了实验验证。实验结果表明,与现有的先进方法相比,该方法具有良好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Network for Makeup Face Verification Based upon Deep Learning
Makeup, derived from the human pursuit of beauty, it changes the image of people appearance, brings more beautiful enjoyment and spiritual pleasure. However, recent studies have shown that facial makeup have a negative effect on face verification. To solve this problem, we formulate an end-to-end deep learning network which is composed of a stem CNN and a novel mapping module. Specifically, we pre-train our framework on a comprehensive dataset and fine-tune our mapping module on makeup datasets. Then we experimentally validate the proposal on these datasets. Experimental results demonstrate that the proposal achieves promising performance compared to the existing state-of-the-art methods.
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