A Two-Stage Emotion Generation Model Combining CGAN and pix2pix

Wang, Yuanqing; Ghani, Dahlan Abdul; Zhou, Bingqian

doi:10.4018/joeuc.330647

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…The large area of covered faces made the existing face recognition systems unable to correctly recognize faces (Liu et al, 2022). For example, facial recognition for phone unlocking and high-speed railway station ticket verification becomes inoperable when masks are worn (Wang et al, 2023). In scenarios where facial payment is required, password payment is also necessary (Yan et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

An Improved Face Mask Detection Simulation Algorithm Based on YOLOv5 Model

Qi,

Wang,

Dong

2024

International Journal of Gaming and Computer-Mediated Simulations

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This paper proposes an advanced approach for detecting faces with mask occlusion based on YOLOv5 to address various challenges encountered in face detection, including illumination blur and occlusion. The proposed methodology involves the integration of a convolutional block attention module into the backbone network and different network levels in the neck of the YOLOv5s. This approach enables the suppression of irrelevant features and emphasizes the identification of masked facial features. Replacing the conventional loss function with the Focal Loss function addresses the problem of sample imbalance. The enhanced YOLOv5s network was applied to detect mask-occluded faces. Empirical evaluations were conducted on the WIDER Face and AIZOO datasets. The simulation comparison results demonstrate that the proposed method achieves superior real-time detection performance, fulfilling the objective of developing a lightweight detection model.

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Section: Introductionmentioning

confidence: 99%

An Improved Face Mask Detection Simulation Algorithm Based on YOLOv5 Model

Qi,

Wang,

Dong

2024

International Journal of Gaming and Computer-Mediated Simulations

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Miner Fatigue Detection from Electroencephalogram-Based Relative Power Spectral Topography Using Convolutional Neural Network

Xu,

Li,

Feng

2023

Sensors

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Fatigue of miners is caused by intensive workloads, long working hours, and shift-work schedules. It is one of the major factors increasing the risk of safety problems and work mistakes. Examining the detection of miner fatigue is important because it can potentially prevent work accidents and improve working efficiency in underground coal mines. Many previous studies have introduced feature-based machine-learning methods to estimate miner fatigue. This work proposes a method that uses electroencephalogram (EEG) signals to generate topographic maps containing frequency and spatial information. It utilizes a convolutional neural network (CNN) to classify the normal state, critical state, and fatigue state of miners. The topographic maps are generated from the EEG signals and contrasted using power spectral density (PSD) and relative power spectral density (RPSD). These two feature extraction methods were applied to feature recognition and four representative deep-learning methods. The results showthat RPSD achieves better performance than PSD in classification accuracy with all deep-learning methods. The CNN achieved superior results to the other deep-learning methods, with an accuracy of 94.5%, precision of 97.0%, sensitivity of 94.8%, and F1 score of 96.3%. Our results also show that the RPSD–CNN method outperforms the current state of the art. Thus, this method might be a useful and effective miner fatigue detection tool for coal companies in the near future.

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Deep Learning-Driven Multi-Stage Product Visual Emotional Design Utilizing Web Semantics

Chen,

Wang,

Zhu

2024

International Journal on Semantic Web and Information Systems

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With the rapid development of artificial intelligence, the generation of emotional expression images has become a key research field. This article introduces a novel multi-stage emotion generation model cascade method that utilizes CGAN, Pix2Pix, and CycleGAN to create images with enhanced emotional depth and visual quality. We have outlined our approach, which involves a continuous process from emotional initialization to texture refinement, and then to style transition. Our experiments on facial and automotive datasets show a significant improvement in image quality compared to traditional models, with an average increase of 40 percentage points in structural similarity (SSIM) and an average increase of 11.1 percentage points in peak signal-to-noise ratio (PSNR). The research findings emphasize the potential applications of our model in advertising, entertainment, and human-computer interaction, where visual effects of emotional resonance are crucial.

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A Two-Stage Emotion Generation Model Combining CGAN and pix2pix

Cited by 3 publications

References 37 publications

An Improved Face Mask Detection Simulation Algorithm Based on YOLOv5 Model

An Improved Face Mask Detection Simulation Algorithm Based on YOLOv5 Model

Miner Fatigue Detection from Electroencephalogram-Based Relative Power Spectral Topography Using Convolutional Neural Network

Deep Learning-Driven Multi-Stage Product Visual Emotional Design Utilizing Web Semantics

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