TY - GEN
T1 - Sex difference of saccade patterns in emotional facial expression recognition
AU - Han, Yaohui
AU - Chen, Badong
AU - Zhang, Xuetao
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2017.
PY - 2017
Y1 - 2017
N2 - In this work, we conduct two experiments: the first aims at emotional (sadness, happiness, disgust, fear, anger, shame) and neutral facial expressions recognition and the second investigates how the emotional audio (cry, laugh) affects the recognition for emotional (sadness, happy) and neutral facial expressions. The eye movements data in both experiments are recorded by an SMI eye tracker with 120 Hz sampling frequency. The hidden Markov model (HMM) is then applied to extract the fixation patterns in data. For each emotional block, two HMMs are learned, which are related, respectively, to the emotional and neutral faces. The HMM models are trained using 70% saccade data and tested using the remaining 30% data. The first experiment indicates that the saccade patterns not only relate to the task, but also relate to the emotional stimulations and contexts. In particular, the males and females show significant difference in that the females are more easily affected by sadness facial expressions (negative emotion). The second experiment maintains that the emotional audio has greater impact on the females than on the males while the subjects recognizing the neutral facial expressions. This implies that men and women also display significant difference in audio modulated facial expressions cognition. The findings in this study may be still inconclusive, possibly the results of methodological differences.
AB - In this work, we conduct two experiments: the first aims at emotional (sadness, happiness, disgust, fear, anger, shame) and neutral facial expressions recognition and the second investigates how the emotional audio (cry, laugh) affects the recognition for emotional (sadness, happy) and neutral facial expressions. The eye movements data in both experiments are recorded by an SMI eye tracker with 120 Hz sampling frequency. The hidden Markov model (HMM) is then applied to extract the fixation patterns in data. For each emotional block, two HMMs are learned, which are related, respectively, to the emotional and neutral faces. The HMM models are trained using 70% saccade data and tested using the remaining 30% data. The first experiment indicates that the saccade patterns not only relate to the task, but also relate to the emotional stimulations and contexts. In particular, the males and females show significant difference in that the females are more easily affected by sadness facial expressions (negative emotion). The second experiment maintains that the emotional audio has greater impact on the females than on the males while the subjects recognizing the neutral facial expressions. This implies that men and women also display significant difference in audio modulated facial expressions cognition. The findings in this study may be still inconclusive, possibly the results of methodological differences.
KW - Facial expression recognition
KW - HMM
KW - Multi-modality
KW - Saccade
UR - https://www.scopus.com/pages/publications/85026787099
U2 - 10.1007/978-981-10-5230-9_16
DO - 10.1007/978-981-10-5230-9_16
M3 - 会议稿件
AN - SCOPUS:85026787099
SN - 9789811052293
T3 - Communications in Computer and Information Science
SP - 144
EP - 154
BT - Cognitive Systems and Signal Processing - 3rd International Conference, ICCSIP 2016, Revised Selected Papers
A2 - Sun, Fuchun
A2 - Liu, Huaping
A2 - Hu, Dewen
PB - Springer Verlag
T2 - 3rd International Conference on Cognitive Systems and Information Processing, ICCSIP 2016
Y2 - 19 November 2016 through 23 November 2016
ER -