TY - GEN
T1 - Building large scale 3D face database for face analysis
AU - Hu, Yuxiao
AU - Zhang, Zhenqiu
AU - Xu, N.
AU - Fu, Yun
AU - Huang, Thomas S.
PY - 2007
Y1 - 2007
N2 - We propose to build a large scale 3D face database with dense correspondence for variant face analysis research purposes. Large scale means that the number of subjects in the database is more than 400, which is, to our best knowledge, the biggest1 one at this time. 3D face means that we provide both the texture and shape of human faces, which is also balanced in gender and race. Dense correspondence means that the key facials points with semantic meanings are carefully labeled and aligned among different faces, which can be used for a broad range of face analysis tasks. We provide the data description, data collection schema and the post-processing methods to help the usage of the data and future extension. More and more data is still being collected and processed to enlarge the extensive 3D face database. The proposed face database provides solid ground truth for human face related tasks such as alignment, tracking, recognition and animation, etc.
AB - We propose to build a large scale 3D face database with dense correspondence for variant face analysis research purposes. Large scale means that the number of subjects in the database is more than 400, which is, to our best knowledge, the biggest1 one at this time. 3D face means that we provide both the texture and shape of human faces, which is also balanced in gender and race. Dense correspondence means that the key facials points with semantic meanings are carefully labeled and aligned among different faces, which can be used for a broad range of face analysis tasks. We provide the data description, data collection schema and the post-processing methods to help the usage of the data and future extension. More and more data is still being collected and processed to enlarge the extensive 3D face database. The proposed face database provides solid ground truth for human face related tasks such as alignment, tracking, recognition and animation, etc.
UR - http://www.scopus.com/inward/record.url?scp=37249072297&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=37249072297&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-73417-8_42
DO - 10.1007/978-3-540-73417-8_42
M3 - Conference contribution
AN - SCOPUS:37249072297
SN - 9783540734161
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 343
EP - 350
BT - Multimedia Content Analysis and Mining - International Workshop, MCAM 2007, Proceedings
PB - Springer
T2 - International Workshop on Multimedia Content Analysis and Mining, MCAM 2007
Y2 - 30 June 2007 through 1 July 2007
ER -