{"title":"基于深度学习的化妆人脸验证网络","authors":"Jiawei Hou, Zhaohui Wang, Yigan Li","doi":"10.1109/ICIVC50857.2020.9177431","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":6806,"journal":{"name":"2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC)","volume":"57 1","pages":"123-127"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A Network for Makeup Face Verification Based upon Deep Learning\",\"authors\":\"Jiawei Hou, Zhaohui Wang, Yigan Li\",\"doi\":\"10.1109/ICIVC50857.2020.9177431\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":6806,\"journal\":{\"name\":\"2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC)\",\"volume\":\"57 1\",\"pages\":\"123-127\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIVC50857.2020.9177431\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIVC50857.2020.9177431","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.