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Stream/Area:
-
Subject:
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Paper ID:
IJSREM65606
Volume:
10 |
6 |
Jun 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Information Technology and Engineering
Goa College Of Engineering
Information Technology and Engineering
Goa College Of Engineering
Facial Expression Recognition (FER) stands at the confluence of computer vision, affective neuroscience, and human-computer interaction, enabling machines to automatically interpret human emotional states from visual and physiological data. Over the past decade, the field has undergone a fundamental paradigm shift, transitioning from handcrafted feature pipelines toward end-to-end deep learning architectures capable of processing complex, unconstrained real-world data. This survey presents a comprehensive and critical analysis of deep FER research, systematically examining the evolution from static convolutional networks to dynamic spatiotemporal models and, most recently, to multimodal systems that fuse facial video with electroencephalography (EEG) signals. We evaluate core pipeline components including face alignment preprocessing, specialized loss formulations such as Island Loss and Locality-Preserving Loss, Peak-Piloted Deep Networks for dynamic sequence modeling, and hierarchical LSTM architectures for video-EEG fusion. A structured comparative analysis of representative systems is presented, benchmarked across standard datasets including CK+, FER2013, RAF-DB, AffectNet, and AFEW. Our synthesis reveals that multimodal fusion consistently yields Classification Rate improvements of at least 2% over unimodal baselines. Key open challenges are identified, including cross-dataset generalization, annotation noise, computational efficiency, and ethical dimensions of affective surveillance. Future research directions centering on unsupervised domain adaptation, action-unit-guided attention, and privacy-preserving federated learning are outlined, providing a roadmap for the next generation of emotionally intelligent systems.
KEYWORDS
This survey has presented a comprehensive critical analysis of the deep learning landscape for Facial Expression Recognition, spanning static image architectures, dynamic spatiotemporal models, and the multimodal video-EEG fusion paradigm. Three key technical insights emerge from this synthesis: (1) specialized loss functions such as Island Loss and LP Loss deliver reliable manifold optimization benefits independent of backbone architecture; (2) the Peak-Piloted Deep Network's gradient suppression strategy provides an elegant solution to expression intensity invariance; and (3) hierarchical LSTM fusion with selective temporal attention achieves measurable gains from EEG integration that complements visual modality limitations. The most productive future research directions lie at the intersection of domain adaptation, non-invasive physiological sensing, interpretable AU-guided architectures, and privacy-preserving federated learning. As emotionally intelligent systems become increasingly embedded in healthcare, transportation, and human-computer interaction, the convergence of these technical advances with appropriate ethical frameworks will define the character of next-generation affective computing. This survey aims to serve as a structured foundation and critical roadmap for researchers navigating this rapidly evolving field
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Publication Date
6/27/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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