Improving facial expression recognition for autism with IDenseNet‐RCAformer under occlusions DOI

S. Selvi,

M. Parvathy

International Journal of Developmental Neuroscience, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 27, 2024

The term 'autism spectrum disorder' describes a neurodevelopmental illness typified by verbal and nonverbal interaction impairments, repetitive behaviour patterns poor social interaction. Understanding mental states from FEs is crucial for interpersonal But when there are occlusions like glasses, facial hair or self-occlusion, it becomes harder to identify expressions accurately. This research tackles the issue of identifying parts face occluded suggests an innovative technique tackle this difficulty. Creating strong framework expression recognition (FER) that better handles increases accuracy goal research. Therefore, we propose novel Improved DenseNet-based Residual Cross-Attention Transformer (IDenseNet-RCAformer) system partial occlusion FER problem in autism patients. framework's efficacy assessed using four datasets expressions, some preprocessing procedures conducted increase efficiency. After that, recognizing simple argmax function applied get forecasted landmark position heatmap. Then feature extraction phase, local global representation captured preprocessed images adopting Inception-ResNet-V2 approach, Transformer, respectively. Moreover, both features fused employing FusionNet method, thereby enhancing system's training speed precision. extracted, improved DenseNet mechanism efficiently recognize variety partially A number performance metrics determined analysed demonstrate proposed approach's effectiveness, where IDenseNet-RCAformer performs best with 98.95%. According experimental findings, significantly outperforms prior frameworks terms outcomes.

Language: Английский

Urban informal settlements interpretation via a novel multi-modal Kolmogorov–Arnold fusion network by exploring hierarchical features from remote sensing and street view images DOI Creative Commons
Haibin Niu, Runyu Fan, Jiajun Chen

et al.

Science of Remote Sensing, Journal Year: 2025, Volume and Issue: unknown, P. 100208 - 100208

Published: Feb. 1, 2025

Language: Английский

Citations

1
Supply-Demand risk assessment of urban flood resilience from the perspective of the ecosystem services: A case study in Nanjing, China DOI
Peng Zhang,

Xukan Xu,

Wentong Yang

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 173, P. 113397 - 113397

Published: March 26, 2025

Language: Английский

Citations

0
Mapping and Analyzing Winter Wheat Yields in the Huang-Huai-Hai Plain: A Climate-Independent Perspective DOI Creative Commons
Yachao Zhao, Xin Du, Qiangzi Li

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(8), P. 1409 - 1409

Published: April 16, 2025

Accurate diagnostics of crop yields are essential for climate-resilient agricultural planning; however, conventional datasets often conflate environmental covariates during model training. Here, we present HHHWheatYield1km, a 1 km resolution winter wheat yield dataset China’s Huang-Huai-Hai Plain spanning 2000–2019. By integrating climate-independent multi-source remote sensing metrics with Random Forest model, calibrated against municipal statistical yearbooks, the exhibits strong agreement county-level records (R = 0.90, RMSE 542.47 kg/ha, MRE 9.09%), ensuring independence from climatic influences robust driver analysis. Using Geodetector, reveal pronounced spatial heterogeneity in climate–yield interactions, highlighting distinct regional disparities: precipitation variability exerts strongest constraints on Henan and Anhui, whereas Shandong Jiangsu exhibit weaker dependencies. In Beijing–Tianjin–Hebei, March temperature emerges as critical determinant variability. These findings underscore need tailored adaptation strategies, such enhancing water-use efficiency inland provinces optimizing agronomic practices coastal regions. With its dual ability to resolve pixel-scale dynamics disentangle drivers, HHHWheatYield1km represents resource precision agriculture evidence-based policymaking face changing climate.

Language: Английский

Citations

0
Mapping urban construction sites in China through geospatial data fusion: Methods and applications DOI
Chaoqun Zhang, Ziyue Chen, Lei Luo

et al.

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 315, P. 114441 - 114441

Published: Sept. 25, 2024

Language: Английский

Citations

3
Improving facial expression recognition for autism with IDenseNet‐RCAformer under occlusions DOI

S. Selvi,

M. Parvathy

International Journal of Developmental Neuroscience, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 27, 2024

The term 'autism spectrum disorder' describes a neurodevelopmental illness typified by verbal and nonverbal interaction impairments, repetitive behaviour patterns poor social interaction. Understanding mental states from FEs is crucial for interpersonal But when there are occlusions like glasses, facial hair or self-occlusion, it becomes harder to identify expressions accurately. This research tackles the issue of identifying parts face occluded suggests an innovative technique tackle this difficulty. Creating strong framework expression recognition (FER) that better handles increases accuracy goal research. Therefore, we propose novel Improved DenseNet-based Residual Cross-Attention Transformer (IDenseNet-RCAformer) system partial occlusion FER problem in autism patients. framework's efficacy assessed using four datasets expressions, some preprocessing procedures conducted increase efficiency. After that, recognizing simple argmax function applied get forecasted landmark position heatmap. Then feature extraction phase, local global representation captured preprocessed images adopting Inception-ResNet-V2 approach, Transformer, respectively. Moreover, both features fused employing FusionNet method, thereby enhancing system's training speed precision. extracted, improved DenseNet mechanism efficiently recognize variety partially A number performance metrics determined analysed demonstrate proposed approach's effectiveness, where IDenseNet-RCAformer performs best with 98.95%. According experimental findings, significantly outperforms prior frameworks terms outcomes.

Language: Английский

Citations

0