DermaNet: Age and Gender Prediction from Skin Images

🚀 Key Features

• Age Prediction: DermaNet estimates the approximate age of individuals based on their skin images, allowing for age-related analysis.
• Gender Prediction: It predicts the gender of individuals (male, female, or unknown), providing valuable demographic insights.
• High-Quality Model: Built on a robust CNN architecture with preprocessing techniques like CLAHE (Contrast Limited Adaptive Histogram Equalization) and Gaussian Blurring for enhanced image quality.
• User-Friendly Interface: Easy-to-use scripts for prediction and analysis, making it accessible to researchers and practitioners alike.

🧠 Model Architecture

DermaNet utilizes a state-of-the-art Convolutional Neural Network (CNN) for feature extraction and prediction tasks:

• Convolutional Layers: Learn intricate patterns in skin images.
• Max-Pooling Layers: Reduce dimensionality and computational cost.
• Batch Normalization: Speed up training and improve generalization.
• Fully Connected Layers: Perform the final predictions for age and gender.
• Dropout Regularization: Reduce overfitting and improve robustness.

🖼️ How It Works

1. Image Preprocessing: Images are enhanced using CLAHE for better contrast, resized to 256x256 pixels, and normalized for deep learning.
2. Model Training: The model is trained on a large dataset of annotated skin images, learning the correlation between age, gender, and skin features.
3. Prediction: After training, the model predicts the age and gender of new images based on learned patterns.

📊 Results & Performance

• Age Prediction: Achieved a low Mean Absolute Error (MAE) for precise age estimation.
• Gender Prediction: High classification accuracy for predicting gender (male, female, or unknown).

📂 How to Use

1. Clone the Repository

2. Install Dependencies

3. Load the Pre-trained Model

4. Make Predictions

⚙️ Technologies Used

• TensorFlow/Keras: For building and training the deep learning model.
• OpenCV: For image preprocessing and enhancement.
• Matplotlib: For visualizing training performance.
• NumPy: For efficient numerical computations.

📈 Training History

• Age MAE: The training and validation Mean Absolute Error (MAE) for age prediction.
• Gender Accuracy: The training and validation accuracy for gender classification.

🚧 Limitations

• Image Quality: Performance may degrade with poor-quality or distorted images.
• Age Range: Predictions are approximate and may not always be precise for every individual.
• Dataset Bias: Results depend on the diversity and quality of the training dataset.

🧑‍🤝‍🧑 Contribute

Suggestions for Contributions:

• Expand the training dataset with diverse images.
• Improve preprocessing techniques for low-quality images.
• Optimize the model architecture for faster inference.
• Integrate additional demographic predictions.

💬 License