Gender and Emotion Recognition from Implicit User Behavior Signals
This work addresses emotion and gender recognition for applications in human-computer interaction and psychology, but it is incremental as it builds on existing sensor-based methods with new insights from partial occlusion.
The study tackled gender and emotion recognition using EEG signals and eye movements from low-cost sensors, finding reliable recognition with gender-specific EEG differences and emotion-characteristic eye movements, including differential processing of negative emotions for females and gender differences under partial face occlusion.
This work explores the utility of implicit behavioral cues, namely, Electroencephalogram (EEG) signals and eye movements for gender recognition (GR) and emotion recognition (ER) from psychophysical behavior. Specifically, the examined cues are acquired via low-cost, off-the-shelf sensors. 28 users (14 male) recognized emotions from unoccluded (no mask) and partially occluded (eye or mouth masked) emotive faces; their EEG responses contained gender-specific differences, while their eye movements were characteristic of the perceived facial emotions. Experimental results reveal that (a) reliable GR and ER is achievable with EEG and eye features, (b) differential cognitive processing of negative emotions is observed for females and (c) eye gaze-based gender differences manifest under partial face occlusion, as typified by the eye and mouth mask conditions.