Face Attraction Detection App

Welcome to Attractiveness Prediction App

The goal of this project is to utilize advanced image processing and machine learning techniques to predict the attractiveness of a face in an image. This prediction is based on several visual features derived from the face, enabling automated attractiveness classification.

Data Exploration and Preprocessing

To begin with, an extensive exploratory data analysis (EDA) was conducted on the dataset, allowing me to better understand the relationship between the features and the target variable (Attractiveness). The dataset included face images along with their respective attractiveness scores. The data was cleaned by converting negative instances (represented as -1) into 0.

Model Building

I worked with several deep learning models to tackle the classification problem. These models include:

• InceptionV3
• ResNet-50
• ResNet-101V2

Each model was fine-tuned by freezing certain layers and adding custom fully connected layers to adapt them for the task at hand.

To ensure that the model can handle various transformations in images such as rotation, flipping, and scaling, I implemented data augmentation. This helped in improving the model's generalization capabilities and performance across diverse face images.

The models were trained using a subset of the dataset, which was split into training, validation, and testing sets. Performance metrics such as accuracy were used to evaluate the models, and the best-performing model was selected.

Performance

The project achieved the following accuracy scores for the respective models:

• InceptionV3: 68.80%
• ResNet-50: 50.20%
• ResNet-101V2: 62.13%

The InceptionV3 model achieved the highest accuracy, demonstrating its ability to effectively classify faces based on attractiveness.

Conclusion

The Attractiveness Prediction Model demonstrates how deep learning can be applied to solve complex image classification tasks. By leveraging pre-trained models such as InceptionV3 and ResNet, along with data augmentation techniques, the model provides reliable predictions. The web-based interface ensures ease of access for users, allowing them to test the model with their own images.

This project highlights my proficiency in deep learning, model deployment, and web application development, making it a valuable addition to my portfolio.