Aug 2026· Journal of Ambient Intelligence and Humanized Computing· Vol 17, pp. 1579 - 1598· 0 citations· 59 references
TL;DR
A lightweight FER model that combines a truncated MobileNetV2 backbone with a patch-based local feature extraction module and a channel-attention refinement module and a channel-attention refinement module, followed by a compact classifier is proposed, outperforming many existing lightweight models.
A hybrid model which uses several ResNet50 mixed with VGG16 backbones to improve the local facial features with the global context for a robust FER solu-tion, and shows superior robustness to occlusions and pose, even under cross-dataset testing.
A. Ouza, Mohamed El Ghmary, A. Choukri· Multimedia tools and applica...· 0 citations
Facial Expression Recognition (FER) plays an important role in human-computer interaction (HCI). However, most highly accurate FER models rely on complex and computationally heavy architectures that makes them unsuitable for low resource devices. This study proposes a modified MobileNetV2-based lightweight network that...
A stability-oriented convolutional study that rethinks model capacity for seven-class small-sample FER and combines stratified partitioning, grayscale normalization, compact VGG-style representation learning, global average pooling, label smoothing, dropout, L2 regularization, and momentum-based optimization to control...
Yang-Xuan Xie· Applied and Computational En...· 0 citations
Purpose: This paper presents and evaluates a cloud-oriented facial expression recognition (FER) system built on a compact, squeeze-style Vision Transformer, and assesses its suitability as an accurate, resource-efficient modelling layer for cloud and edge-cloud deployment.
Design/Methodology/Approach: A pretrained ViT-...
S. Al-darraji, A. J. Jalil· African Journal Of Applied R...· 0 citations
The FER20E dataset provides a comprehensive benchmark for advancing emotion recognition in unconstrained and real-world scenarios, and a data annotation tool (DL-DAT) that follows a semi-automated, human-in-the-loop pipeline to enable scalable and reliable annotation.
Facial emotion recognition (FER) plays a pivotal role in affective computing, healthcare monitoring, adaptive education, surveillance, and human computer interaction. This study benchmarks three deep learning solutions for seven class FER on the Extended Cohn Kanade (CK+) corpus: a compact custom convolutional neural n...
Amit Rehapade· Natural Resources for Human...· 0 citations
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