  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 gender-specific 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 ($\approx$70%) gender recognition is achievable on
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.
