Gender and emotion recognition with implicit user signals

Maneesh Bilalpur, Seyed Mostafa Kia, Manisha Chawla, Tat-Seng Chua, Ramanathan Subramanian
2017 Proceedings of the 19th ACM International Conference on Multimodal Interaction - ICMI 2017  
We examine the utility of implicit user behavioral signals captured using low-cost, o -the-shelf devices for anonymous gender and emotion recognition. A user study designed to examine male and female sensitivity to facial emotions con rms that females recognize (especially negative) emotions quicker and more accurately than men, mirroring prior ndings. Implicit viewer responses in the form of EEG brain signals and eye movements are then examined for existence of (a) emotion and gender-speci c
more » ... erns from event-related potentials (ERPs) and xation distributions and (b) emotion and gender discriminability. Experiments reveal that (i) Gender and emotion-speci c di erences are observable from ERPs, (ii) multiple similarities exist between explicit responses gathered from users and their implicit behavioral signals, and (iii) Signicantly above-chance (≈70%) gender recognition is achievable on comparing emotion-speci c EEG responses-gender di erences are encoded best for anger and disgust. Also, fairly modest valence (positive vs negative emotion) recognition is achieved with EEG and eye-based features.
doi:10.1145/3136755.3136790 dblp:conf/icmi/BilalpurKCCS17 fatcat:uqwv23vgbzhchjjo2yftw3igo4