Gender Classification Model
Designed and trained a Convolutional Neural Network for binary gender classification from facial images. The pipeline includes data preprocessing, augmentation, model architecture design, training with early stopping, and comprehensive evaluation with confusion matrices and ROC curves.
Tech Stack
The Problem
Accurate gender classification from facial images is a foundational computer vision task with applications in demographics analysis, personalized user experiences, and security systems.
Approach
Built a CNN architecture with multiple convolutional blocks, batch normalization, and dropout layers. Implemented a complete training pipeline with data augmentation to improve generalization, and evaluated using precision, recall, F1-score, and AUC-ROC metrics.
Key Challenges
- Handling class imbalance in the training dataset
- Preventing overfitting with limited data
- Optimizing inference speed for real-time prediction
Results
Achieved high classification accuracy with robust generalization across diverse facial images. Model deployed with a simple prediction interface for real-time gender classification.