Enterprise AI Analysis
Application of a Hybrid CNN-SE-GCN Model with Multi-Loss Optimization and Adversarial Samples in Real-Time Emotion Recognition
This paper introduces a novel hybrid CNN-SE-GCN model for real-time facial expression recognition, tackling challenges like small samples, imbalanced datasets, and adversarial attacks. Inspired by GANs, the model uses an improved ResNet18 with SE modules for local feature extraction and GCNs for geometric information, fusing global and local features effectively. A geometric information discriminator enhances robustness through adversarial training. A multi-loss optimization strategy (classification, geometric constraint, adversarial training) is proposed. Experiments on the CK+ dataset show high accuracy and robustness. A web system is also developed for practical application. This innovation significantly improves adaptability and accuracy in complex scenarios, making it suitable for intelligent healthcare and affective computing.
Executive Impact: Key Performance Indicators
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97.12%
Improved Accuracy on CK+ Dataset
0.98
AUC Score for Classification
Enhanced
Robustness to Adversarial Attacks
Real-time
Web System for Practical Use
Deep Analysis & Enterprise Applications
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Model Architecture
Loss Optimization & Adversarial Training
Real-time Application & Web System
The proposed hybrid CNN-SE-GCN model combines Convolutional Neural Networks (CNNs) with Squeeze-and-Excitation (SE) modules and Graph Convolutional Networks (GCNs). CNNs (specifically, an improved ResNet18) extract local pixel-level features, while SE modules dynamically adjust channel weights. GCNs process high-dimensional geometric information, fusing global and local features. Inspired by GANs, it includes a geometric information discriminator for enhanced robustness through adversarial training.
Enterprise Process Flow
Input Image (Batch, 3, 224, 224)
→
ResNet18 Layers with SE Modules
→
512-D Feature Vector (Batch, 512)
→
Graph Construction
→
GCN Layer 1 (GraphConv)
→
GCN Layer 2 (GraphConv)
→
Classification (Softmax Output)
97.12%
Achieved Accuracy
The CNN-SE-GCN model with all loss optimizations reached an accuracy of 97.12% on the CK+ dataset.
| Component | Functionality | Key Contribution |
|---|---|---|
| Generator (CNN-SE-GCN) | Extracts local and global features, classifies emotions. |
|
| Discriminator (Geometric Info) | Distinguishes generated features from real geometric info. |
|
A multi-loss optimization strategy is employed, integrating classification loss (Focal Loss), geometric constraint loss (MSE Loss), and adversarial training loss (BCE Loss with Gradient Penalty). Focal Loss addresses class imbalance, MSE Loss ensures geometric consistency, and adversarial training (using FGSM-generated samples and BCE Loss + GP) enhances robustness against perturbations. This comprehensive strategy optimizes feature representation and model resilience.
0.98
AUC Score
The model achieved an AUC score of 0.98, indicating excellent discriminative ability.
| Loss Type | Purpose | Key Benefit |
|---|---|---|
| Focal Loss | Classification & Class Imbalance |
|
| Geometric Constraint Loss (MSE) | Feature Similarity & Consistency |
|
| Adversarial Training Loss (BCE + GP) | Discriminator Training & Robustness |
|
The proposed method is integrated into a real-time facial expression recognition web system. This system applies the model to practical scenarios, offering an efficient solution for emotion recognition. The system aims to expand into psychological health monitoring and human-computer interaction, demonstrating the practical value and potential for intelligent suggestion generation based on emotion recognition results.
Real-time Emotion Recognition System
The study successfully developed a real-time web system integrating the proposed CNN-SE-GCN model. This system provides an efficient solution for real-time facial expression recognition tasks. Future plans include expanding its application into psychological health monitoring and human-computer interaction, demonstrating strong practical value.
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