Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 219-222 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781509021789 |
| DOIs | |
| Publication status | Published - 2016 Aug 16 |
| Event | 2nd IEEE International Conference on Multimedia Big Data, BigMM 2016 - Taipei, Taiwan Duration: 2016 Apr 20 → 2016 Apr 22 |
Publication series
| Name | Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016 |
|---|
Other
| Other | 2nd IEEE International Conference on Multimedia Big Data, BigMM 2016 |
|---|---|
| Country | Taiwan |
| City | Taipei |
| Period | 16-04-20 → 16-04-22 |
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All Science Journal Classification (ASJC) codes
- Signal Processing
- Information Systems
- Media Technology
Cite this
}
A framework of enlarging face datasets used for makeup face analysis. / Hu, Min-Chun; Wu, Hsin Ting; Lo, Li Yun; Pan, Tse Yu; Cheng, Wen Huang; Hua, Kai Lung; Mei, Tao.
Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 219-222 7545025 (Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - A framework of enlarging face datasets used for makeup face analysis
AU - Hu, Min-Chun
AU - Wu, Hsin Ting
AU - Lo, Li Yun
AU - Pan, Tse Yu
AU - Cheng, Wen Huang
AU - Hua, Kai Lung
AU - Mei, Tao
PY - 2016/8/16
Y1 - 2016/8/16
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84987678181&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84987678181&partnerID=8YFLogxK
U2 - 10.1109/BigMM.2016.62
DO - 10.1109/BigMM.2016.62
M3 - Conference contribution
T3 - Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016
SP - 219
EP - 222
BT - Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -