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@article{EmoVison2025,

title = {EmoVisioNet: A hybrid network unifying lightweight CNN and attention-based vision model for facial emotion detection},

author = {Gargi Mishra and Supriya Bajpai and Dharmender Saini and Rachna Jain and Deepak Kumar Jain and Vitomir Štruc },

doi = {https://doi.org/10.1016/j.neucom.2025.132224},

year  = {2026},

date = {2026-02-07},

urldate = {2025-11-28},

journal = {Neurocomputing},

volume = {665},

number = {132224},

pages = {1-12},

abstract = {Facial emotion detection has witnessed a surge in demand across numerous applications, including human-computer interaction, healthcare, and security. Accurate expression recognition is crucial for improving human-computer interactions and understanding human behavior. Existing facial emotion detection models face challenges in achieving both high accuracy and real-time processing due to complex architectures. Our goal is to create an efficient yet accurate solution that can work on resource-constrained devices. To address the challenge of accurately recognizing emotions from facial expressions, we propose a novel hybrid approach that combines the strengths of pretrained Lightweight Convolutional Neural Networks (CNN), and Attention-based Vision Models. The pretrained Lightweight CNN serves as a feature extractor, efficiently capturing facial features, while the attention model refines the feature representation to focus on crucial regions of the face associated with different expressions. This enables our model to achieve state-of-the-art (SOTA) accuracy with reduced computational requirements. The proposed model, EmoVisioNet, achieves superior performance across multiple datasets, attaining 99.97 % accuracy on CK+, 96.23 % on RAF-DB, 93.88 % on FER2013, and 96.91 % on FERPlus. The obtained results surpass the current state-of-the-art in this field, demonstrating the EmoVisioNet’s superior performance in facial expression recognition.},

keywords = {CNN, deep learning, facial expression recognition, lightweight models},

pubstate = {published},

tppubtype = {article}

}

@article{Vitek2024_Gaze,

title = {GazeNet: A lightweight multitask sclera feature extractor},

author = {Matej Vitek and Vitomir Štruc and Peter Peer},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2024/11/1-s2.0-S1110016824014273-main.pdf

https://www.sciencedirect.com/science/article/pii/S1110016824014273},

doi = {https://doi.org/10.1016/j.aej.2024.11.011},

year  = {2025},

date = {2025-01-05},

journal = {Alexandria Engineering Journal},

volume = {112},

pages = {661-671},

abstract = {The sclera is a recently emergent biometric modality with many desirable characteristics. However, most literature solutions for sclera-based recognition rely on sequences of complex deep networks with significant computational overhead. In this paper, we propose a lightweight multitask-based sclera feature extractor. The proposed GazeNet network has a computational complexity below 1 GFLOP, making it appropriate for less capable devices like smartphones and head-mounted displays. Our experiments show that GazeNet (which is based on the SqueezeNet architecture) outperforms both the base SqueezeNet model as well as the more computationally intensive ScleraNET model from the literature. Thus, we demonstrate that our proposed gaze-direction multitask learning procedure, along with careful lightweight architecture selection, leads to computationally efficient networks with high recognition performance.},

keywords = {biometrics, CNN, deep learning, lightweight models, sclera},

pubstate = {published},

tppubtype = {article}

}