Gender and Emotion Recognition with Implicit User Signals [article]

Maneesh Bilalpur, Seyed Mostafa Kia, Manisha Chawla, Tat-Seng Chua and Ramanathan Subramanian
2017 arXiv   pre-print
We examine the utility of implicit user behavioral signals captured using low-cost, off-the-shelf devices for anonymous gender and emotion recognition. A user study designed to examine male and female sensitivity to facial emotions confirms that females recognize (especially negative) emotions quicker and more accurately than men, mirroring prior findings. Implicit viewer responses in the form of EEG brain signals and eye movements are then examined for existence of (a) emotion and
more » ... ic patterns from event-related potentials (ERPs) and fixation distributions and (b) emotion and gender discriminability. Experiments reveal that (i) Gender and emotion-specific differences are observable from ERPs, (ii) multiple similarities exist between explicit responses gathered from users and their implicit behavioral signals, and (iii) Significantly above-chance (≈70 comparing emotion-specific EEG responses-- gender differences are encoded best for anger and disgust. Also, fairly modest valence (positive vs negative emotion) recognition is achieved with EEG and eye-based features.
arXiv:1708.08735v1 fatcat:zd7qf4x7mfaq5kwr55ycfr47ba