TY - GEN
T1 - Learning Guided Attention Masks for Facial Action Unit Recognition
AU - Lakshminarayana, Nagashri
AU - Setlur, Srirangaraj
AU - Govindaraju, Venu
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - Humans have the innate ability to rapidly spot and react to a person's emotional response. For computers to be able to understand expressions in a similar way, the gap between the perception of expressions between the humans and computers needs to be minimized. Inspired by human visual fixations, we propose a guided attention mechanism that facilitates the network to 'look' at the most important features of a face. Rather than imposing hard attention, we learn the attention maps from the intermediate representation for Action Units (AUs). We propose a joint attention learning and AU classification module with minimal increase in the network parameters. We demonstrate the efficiency of our approach on three standard datasets: BP4D, MMSE and DISFA and obtain state of the art and near state of the art results respectively.
AB - Humans have the innate ability to rapidly spot and react to a person's emotional response. For computers to be able to understand expressions in a similar way, the gap between the perception of expressions between the humans and computers needs to be minimized. Inspired by human visual fixations, we propose a guided attention mechanism that facilitates the network to 'look' at the most important features of a face. Rather than imposing hard attention, we learn the attention maps from the intermediate representation for Action Units (AUs). We propose a joint attention learning and AU classification module with minimal increase in the network parameters. We demonstrate the efficiency of our approach on three standard datasets: BP4D, MMSE and DISFA and obtain state of the art and near state of the art results respectively.
KW - affective computing
KW - Facial action unit recognition
KW - spatial attention
UR - https://www.scopus.com/pages/publications/85101475620
U2 - 10.1109/FG47880.2020.00128
DO - 10.1109/FG47880.2020.00128
M3 - Conference contribution
AN - SCOPUS:85101475620
T3 - Proceedings - 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2020
SP - 465
EP - 472
BT - Proceedings - 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2020
A2 - Struc, Vitomir
A2 - Gomez-Fernandez, Francisco
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2020
Y2 - 16 November 2020 through 20 November 2020
ER -