Christopher Mendoza, Ian Lucas
Date: December 11, 2024

This project explores the prediction of facial beauty scores using deep learning techniques in computer vision. The goal is to develop a machine learning model capable of scoring facial images based on human-perceived beauty using the SCUT-FBP5500 dataset.

• Name: SCUT-FBP5500 Dataset (Available on Kaggle)
• Description: A dataset of facial images labeled with beauty scores.
• Structure:
  • Image Name: Filename of the image (e.g., img_1.jpg).
  • Beauty Score: A numerical value representing the beauty score (e.g., 4.2).

• Understand the dataset structure and beauty score distribution.
• Visualize correlations between beauty scores and image characteristics (e.g., brightness, contrast).
• Inspect example images grouped by varying beauty scores.

• Beauty Score Distribution: Analyze frequency and range of scores.
• Correlation Analysis: Determine if image properties influence beauty scores.
• Sample Image Visualization: Display images alongside their scores for qualitative insights.

1. Image Resizing: All images were resized to 224x224 pixels.
2. Normalization: Pixel values were scaled to a range of [0, 1].
3. Data Augmentation:
   • Applied transformations: rotation, flipping, shifting, and zooming.
   • Visualized augmented samples to ensure diversity in the training set.
4. Dataset Splitting:
   • Train (60%), Validation (20%), Test (20%) splits.
   • Ensured balanced beauty score distribution across splits.

• Resizing and normalization standardize input for the model.
• Augmentation improves model robustness and reduces overfitting.
• Splitting ensures unbiased evaluation and reproducibility.

• Feature-Enhanced Linear Regression:
  • Features extracted from a pretrained VGG16 model.
  • Linear regression as the predictive layer.
• Challenges:
  • Linear regression couldn’t capture the non-linear nature of beauty perception.
  • Limited ability to optimize features for beauty scoring.

• Deep Learning Model:
  • Fine-tuned VGG16 pretrained model for end-to-end learning.
  • Used Convolutional Neural Networks (CNNs) for feature extraction and regression.
  • Implemented a weighted loss function to handle score imbalances.

• Mean Absolute Error (MAE): Measures the average difference between predicted and actual scores.
• Root Mean Squared Error (RMSE): Highlights the magnitude of prediction errors.
• R-squared (R²): Indicates how well the model explains the variance in beauty scores.

1. Feature Maps: Intermediate convolutional layers’ activations for a test image.
2. Predictions vs. Actual Scores: Scatter plots to assess model accuracy.
3. Top 5 and Bottom 5 Images: Comparison of the best and worst predictions.

• Subjectivity in beauty scoring creates inherent noise in the data.
• Dataset imbalance in beauty scores affects prediction accuracy.

• Train on a more diverse dataset to improve generalization.
• Explore ensemble models combining CNNs with handcrafted features (e.g., symmetry, texture).
• Apply transfer learning with recent architectures like ResNet or EfficientNet.

• Python 3.8
• TensorFlow/Keras
• NumPy, Matplotlib
• scikit-learn

Install dependencies using:

pip install -r requirements.txt