TY - GEN
T1 - Static facial expression analysis in tough conditions
T2 - 2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011
AU - Dhall, Abhinav
AU - Goecke, Roland
AU - Lucey, Simon
AU - Gedeon, Tom
PY - 2011
Y1 - 2011
N2 - Quality data recorded in varied realistic environments is vital for effective human face related research. Currently available datasets for human facial expression analysis have been generated in highly controlled lab environments. We present a new static facial expression database Static Facial Expressions in the Wild (SFEW) extracted from a temporal facial expressions database Acted Facial Expressions in the Wild (AFEW) [9], which we have extracted from movies. In the past, many robust methods have been reported in the literature. However, these methods have been experimented on different databases or using different protocols within the same databases. The lack of a standard protocol makes it difficult to compare systems and acts as a hindrance in the progress of the field. Therefore, we propose a person independent training and testing protocol for expression recognition as part of the BEFIT workshop. Further, we compare our dataset with the JAFFE and Multi-PIE datasets and provide baseline results.
AB - Quality data recorded in varied realistic environments is vital for effective human face related research. Currently available datasets for human facial expression analysis have been generated in highly controlled lab environments. We present a new static facial expression database Static Facial Expressions in the Wild (SFEW) extracted from a temporal facial expressions database Acted Facial Expressions in the Wild (AFEW) [9], which we have extracted from movies. In the past, many robust methods have been reported in the literature. However, these methods have been experimented on different databases or using different protocols within the same databases. The lack of a standard protocol makes it difficult to compare systems and acts as a hindrance in the progress of the field. Therefore, we propose a person independent training and testing protocol for expression recognition as part of the BEFIT workshop. Further, we compare our dataset with the JAFFE and Multi-PIE datasets and provide baseline results.
UR - http://www.scopus.com/inward/record.url?scp=84856645363&partnerID=8YFLogxK
U2 - 10.1109/ICCVW.2011.6130508
DO - 10.1109/ICCVW.2011.6130508
M3 - Conference contribution
SN - 9781467300629
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 2106
EP - 2112
BT - 2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011
Y2 - 6 November 2011 through 13 November 2011
ER -