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Machine Learning-Based Facial Beauty Prediction and Analysis of Frontal Facial Images Using Facial Landmarks and

Tharun J Iyer1, Rahul K2, Ruban Nersisson1

  • 1School of Electrical Engineering, Vellore Institute of Technology, Vellore 632014, India.

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Facial attractiveness prediction is enhanced by combining facial landmarks with features like ratios, texture, shape, and color. K-Nearest Neighbors (KNN) model achieved the best results in predicting facial beauty.

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Area of Science:

  • Computer Vision
  • Biomedical Engineering
  • Aesthetics

Background:

  • The beauty industry's growth fuels interest in quantifying facial attractiveness.
  • Digital media, plastic surgery, and cosmetics increase the demand for facial beauty analysis.
  • Facial attractiveness assessment integrates scientific, artistic, and medical perspectives.

Purpose of the Study:

  • To analyze techniques for assessing facial beauty using facial ratios and qualities.
  • To identify optimal feature sets and machine learning models for predicting facial beauty.
  • To explore how machine learning models interpret the concept of facial beauty.

Main Methods:

  • Facial landmarks extracted to compute Golden Ratios and Symmetry Ratios.
  • Texture (GLCM), shape (Hu's Moments), and color (HSV histograms) features extracted.
  • Ablation studies performed to determine best feature combinations and models (KNN, LR, RF, ANN) on 5500 images.

Main Results:

  • Concatenating primary facial characteristics with facial landmarks significantly improved beauty prediction scores.
  • K-Nearest Neighbors (KNN) model demonstrated superior performance with concatenated features.
  • KNN achieved a Pearson's Correlation Coefficient of 0.7836 and Mean Squared Error of 0.0963.

Conclusions:

  • Combining geometric facial features with image-derived attributes enhances facial attractiveness prediction.
  • Machine learning models, particularly KNN, can effectively learn and predict human perception of facial beauty.
  • This research provides a quantitative approach to facial beauty assessment with implications for various industries.