A framework of enlarging face datasets used for makeup face analysis

Min Chun Hu, Hsin Ting Wu, Li Yun Lo, Tse Yu Pan, Wen Huang Cheng, Kai Lung Hua, Tao Mei

研究成果: Conference contribution

摘要

There have been lots of research about face detection and face recognition. However, faces with makeup usually seriously affect the face recognition result. If we want to recognize the face with higher accuracy, it would be better to first know whether the input face is with makeup or not, and we can use corresponding makeup face or non-makeup face model to recognize it. Unfortunately, the current available datasets for face analysis do not include enough makeup and non-makeup image pairs of users. In this work, we propose a framework to efficiently increase pairs of makeup and non-makeup face images for the existing makeup face datasets. Patch-based features are extracted and support vector machine (SVM) is applied to classify whether a face image is with makeup. The technique of partial least squares (PLS) is then employed to authenticate whether a makeup photo and a non-makeup photo belong to the same person. By combining the makeup detection and the face authentication methods, we can successfully construct a larger face dataset that can be specifically used for applications of makeup face analysis.

原文English
主出版物標題Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016
發行者Institute of Electrical and Electronics Engineers Inc.
頁面219-222
頁數4
ISBN(電子)9781509021789
DOIs
出版狀態Published - 2016 八月 16
事件2nd IEEE International Conference on Multimedia Big Data, BigMM 2016 - Taipei, Taiwan
持續時間: 2016 四月 202016 四月 22

出版系列

名字Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016

Other

Other2nd IEEE International Conference on Multimedia Big Data, BigMM 2016
國家Taiwan
城市Taipei
期間16-04-2016-04-22

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Media Technology

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