Enterprise AI Analysis
A DLF multi-scale quantitative research method for the big five personality traits
Tao Ning, ZhengHua Guo & QiDong Hou
Received: 8 July 2025; Accepted: 6 November 2025; Published online: 03 January 2026
Executive Impact Summary
This research introduces a DLF Multi-Scale Network Model for personality trait analysis, leveraging multimodal features and advanced deep learning techniques to enhance recognition accuracy and robustness in digital environments.
0.9177
Avg Multimodal Fusion Accuracy
0.1644
Mean Absolute Error (MAE)
0.9043
Coefficient of Determination (R²)
Deep Analysis & Enterprise Applications
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Leveraging Advanced AI for Personality Insights
The DLF Multi-Scale Network Model utilizes a sophisticated combination of Discrete Wavelet Transform (DWT), Convolutional Neural Networks (CNNs), and a Lightweight Multi-scale Adapter (LMSA) to achieve superior accuracy in recognizing Big Five personality traits from video data. This advanced architecture enables granular feature extraction and robust temporal alignment, critical for capturing subtle behavioral cues.
Enterprise Process Flow: Proposed DLF Model Workflow
Audio Signals Input
→
Visual Signals Input
→
Audio Data Preprocessing (MLP)
→
Visual Data Preprocessing (DWTC)
→
Latent Feature Sequence Alignment (LMSA)
→
Decision-Level Fusion
→
Personality Trait Recognition
| Method | Avg Accuracy | R2 | MAE |
|---|---|---|---|
| DWTC | 0.9177 | 0.9043 | 0.1644 |
| GP | 0.8862 | 0.8815 | 0.1759 |
| BP | 0.8828 | 0.8782 | 0.1705 |
| LW | 0.8942 | 0.8903 | 0.1689 |
| CD | 0.8796 | 0.8751 | 0.1693 |
| Parameter | Value | Avg Accuracy | Description |
|---|---|---|---|
| Window Size | 1 | 0.8825 | Fails to capture inter-frame correlations. |
| Window Size | 3 (Optimal) | 0.9177 | Precisely aligns micro-expressions and short-term actions. |
| Window Size | 7 | 0.8958 | Introduces redundant frames, diluting effective features. |
| Num Heads | 1 | 0.8915 | Fails to capture trait-specific feature associations. |
| Num Heads | 4 (Optimal) | 0.9177 | Efficiently covers multi-subspace personality correlation features. |
| Num Heads | 8 | 0.9073 | Negligible gain with low parameter efficiency. |
| Fusion Strategy | Avg Accuracy | R2 | MAE |
|---|---|---|---|
| Early Fusion | 0.9103 | 0.9063 | 0.1605 |
| Voting Fusion | 0.9028 | 0.8889 | 0.1611 |
| Decision-Level Fusion | 0.9177 | 0.9043 | 0.1644 |
| Model | Avg Accuracy | E | A | N | C | O |
|---|---|---|---|---|---|---|
| Gorbova et al. | 0.881 | 0.878 | 0.877 | 0.894 | 0.875 | 0.883 |
| Nhi N.Y.Vo et al. | 0.8845 | 0.8816 | 0.8958 | 0.8772 | 0.8814 | 0.8864 |
| Daniel Helm et al. | 0.8905 | 0.8883 | 0.8986 | 0.8886 | 0.8856 | 0.8916 |
| YağmuGüçlütürk | 0.9109 | 0.9107 | 0.9102 | 0.9138 | 0.9089 | 0.9111 |
| Zhao, X. | 0.9167 | 0.9175 | 0.9177 | 0.9163 | 0.9150 | 0.9167 |
| DLF (our) | 0.9177 | 0.9189 | 0.9198 | 0.9160 | 0.9182 | 0.9159 |
Optimized Computational Efficiency
65.60 GFLOPsThe DLF model demonstrates superior computational efficiency with fewer parameters (29.03 million) and lower GFLOPs (65.60) compared to SWINT+LMSA (72.30 GFLOPs), while maintaining higher accuracy and faster training. This represents a strong balance between performance and resource utilization for enterprise deployment.
Addressing Model Limitations & Future Outlook
The current model has limitations in its coverage of special population samples (e.g., facial paralysis, speech disorders), making full validation of generalization capabilities challenging. This restricts its applicability to diverse user groups.
The 'audio-visual separation video frame sampling' pipeline may lead to a loss of critical dynamic information, such as subtle micro-expressions and body movements, which are vital for personality trait recognition. Directly processing video streams is a future goal.
There is a lack of sufficient technical and computational resources for directly extracting video features, hindering full exploitation of spatio-temporal information. Future work will focus on optimizing resource utilization for high-fidelity video processing.
Future research aims to enhance the model by dynamically adjusting the LMSA window size, incorporating textual modality for creativity-related cues, and improving coverage for diverse populations to ensure broader applicability and robustness.
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Your Enterprise AI Adoption Roadmap
A phased approach to integrate cutting-edge AI, ensuring minimal disruption and maximum impact.
Phase 01: Strategic Assessment & Planning
Conduct a comprehensive analysis of existing systems and data infrastructure. Define clear objectives, KPIs, and resource allocation for AI integration.
Phase 02: Pilot Program & Custom Model Training
Develop a proof-of-concept using a subset of your data. Train and fine-tune DLF models with your specific multimodal datasets for optimal performance.
Phase 03: Scaled Deployment & Integration
Integrate the validated AI models into production environments. Ensure seamless compatibility with existing enterprise applications and workflows.
Phase 04: Performance Monitoring & Iteration
Establish continuous monitoring for model performance and data drift. Implement feedback loops for ongoing optimization and updates.
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