projects
Age Classification Model Comparison
Evaluated three computer vision approaches for age group classification using traditional machine learning and deep learning.
- `Python
- OpenCV
- PyTorch
- Scikit-learn
- NumPy
- SVM
- CNN
- HOG
- SIFT
- Bag of Visual Words`
Overview
Designed and conducted a comparative study of three different approaches for classifying facial images into age groups.
Approaches compared
- SVM + HOG — a Support Vector Machine trained on Histogram of Oriented Gradients features
- BoVW + SIFT — a Bag of Visual Words model built on SIFT feature extraction
- CNN — a Convolutional Neural Network trained end to end
Method
Each model was trained, evaluated and compared using the same dataset, analysing classification accuracy, computational performance and the trade-offs between traditional computer vision techniques and modern deep learning methods.
Status
Completed.