2017/08/29 by Maneesh Bilalpur, Bilalpur, Maneesh, Seyed Mostafa Kia +8 · 1 citation
Computer Science · Neuroscience · Psychology · #EEG and Brain-Computer Interfaces #Emotion and Mood Recognition #FOS: Computer and information sciences #Face Recognition and Perception #H.1.2 #H.5.2 #Human-Computer Interaction (cs.HC) #I.3.6 #cs.HC
paper · pdf · doi:10.48550/arxiv.1708.08735
To be published in the Proceedings of 19th International Conference on Multimodal Interaction.2017
arxiv created 2017/08/29 · openalex publication_date 2017/08/29 · arxiv updated 2017/08/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 (≈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.