TY - GEN
T1 - A novel dataset for real-life evaluation of facial expression recognition methodologies
AU - Siddiqi, Muhammad Hameed
AU - Ali, Maqbool
AU - Idris, Muhammad
AU - Banos, Oresti
AU - Lee, Sungyoung
AU - Choo, Hyunseung
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - One limitation seen among most of the previous methods is that they were evaluated under settings that are far from real-life scenarios. The reason is that the existing facial expression recognition (FER) datasets are mostly pose-based and assume a predefined setup. The expressions in these datasets are recorded using a fixed camera deployment with a constant background and static ambient settings. In a real-life scenario, FER systems are expected to deal with changing ambient conditions, dynamic background, varying camera angles, different face size, and other human-related variations. Accordingly, in this work, three FER datasets are collected over a period of six months, keeping in view the limitations of existing datasets. These datasets are collected from YouTube, real world talk shows, and real world interviews. The most widely used FER methodologies are implemented, and evaluated using these datasets to analyze their performance in real-life situations.
AB - One limitation seen among most of the previous methods is that they were evaluated under settings that are far from real-life scenarios. The reason is that the existing facial expression recognition (FER) datasets are mostly pose-based and assume a predefined setup. The expressions in these datasets are recorded using a fixed camera deployment with a constant background and static ambient settings. In a real-life scenario, FER systems are expected to deal with changing ambient conditions, dynamic background, varying camera angles, different face size, and other human-related variations. Accordingly, in this work, three FER datasets are collected over a period of six months, keeping in view the limitations of existing datasets. These datasets are collected from YouTube, real world talk shows, and real world interviews. The most widely used FER methodologies are implemented, and evaluated using these datasets to analyze their performance in real-life situations.
KW - Facial expression recognition
KW - Feature extraction
KW - Feature selection
KW - Real-world
KW - Recognition
KW - YouTube
UR - https://www.scopus.com/pages/publications/84969988384
U2 - 10.1007/978-3-319-34111-8_12
DO - 10.1007/978-3-319-34111-8_12
M3 - Conference contribution
AN - SCOPUS:84969988384
SN - 9783319341101
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 89
EP - 95
BT - Advances in Artificial Intelligence - 29th Canadian Conference on Artificial Intelligence, Canadian AI 2016, Proceedings
A2 - Khoury, Richard
A2 - Drummond, Christopher
PB - Springer Verlag
T2 - 29th Canadian Conference on Artificial Intelligence, AI 2016
Y2 - 31 May 2016 through 3 June 2016
ER -