Facial expression recognition plays a crucial role in many applications, from improving user experience in technology to enhancing mental health assessments. In this guide, we will walk through the process of implementing the EfficientFace model based on the work of Zhao et al. In doing so, we’ll make it as user-friendly as possible. Are you ready? Let’s dive in!

Requirements

• Python: 3.6
• PyTorch: 1.2
• torchvision: 0.4.0

Training Steps

To train the EfficientFace model, follow these systematic steps:

Step 1: Download the Basic Emotions Dataset

First, you need the RAF-DB dataset. Download it from RAF-DB, ensuring its structure mimics the following format:

• RAF-DB
  • train
    • 0
      • train_09748.jpg
      • … (additional images)
      • train_12271.jpg
    • 1
    • … (emotional classes)
    • 6
  • test
    • 0
    • … (additional classes)
    • 6

Note: The numbers represent emotions: 0 (Neutral), 1 (Happiness), 2 (Sadness), 3 (Surprise), 4 (Fear), 5 (Disgust), and 6 (Anger).

Step 2: Download the Pre-trained Model

Next, obtain the pre-trained model from Google Drive and place it into your .checkpoint directory.

Step 3: Configure the Path

Now, in the run.sh file, change the –data argument to your specific dataset path.

Step 4: Execute the Script

Finally, run the script by executing the following command in your terminal:

sh run.sh

Updates

The project frequently receives updates. As of May 5, 2023, there are additional test and visualization codes to enhance usability.

Pre-trained Models Availability

The model includes pre-trained ResNet-18 and ResNet-50 on the MS-Celeb-1M dataset. You can access them through the following links:

• ResNet-18: Google Drive
• ResNet-50: Google Drive

Performance Metrics

The paper reported testing accuracy as follows:

• RAF-DB: 88.36%
• CAER-S: 85.87%
• AffectNet-7: 63.70%
• AffectNet-8: 59.89%

Troubleshooting

If you encounter issues during setup or execution, try the following solutions:

• Ensure that all paths in run.sh accurately point to your data and model files.
• Confirm you have the required versions of Python, PyTorch, and torchvision installed.
• If the model does not perform as expected, verify that the dataset was correctly structured and all files are accessible.

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Conclusion

With the right setup and configuration, you can effectively utilize the EfficientFace model for facial expression recognition. This powerful tool equips researchers and developers with the capability to create systems that understand human emotions, paving the way for emotionally intelligent applications.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.