Face recognition system handling aging and appearance changes using deep learning.
Face recognition systems have become an essential tool in modern security and surveillance applications. However, most existing systems struggle when a person's appearance changes over time due to aging, facial hair, accessories, or environmental conditions.
This project focuses on building a robust face recognition system capable of identifying individuals despite significant variations in age and appearance.
In real-world scenarios, especially in law enforcement, face recognition plays a critical role in:
- Identifying suspects from CCTV footage
- Tracking individuals across different locations
- Finding missing persons
However, these systems often fail when:
- The person has aged significantly
- Appearance changes (e.g., beard, glasses)
- Images are low quality or captured under poor lighting
This project aims to address these challenges and improve recognition performance under such conditions.
- Build a face recognition pipeline using deep learning
- Handle aging and appearance variations
- Evaluate performance under real-world conditions
- Analyze limitations and possible improvements
Image -> Face Detection -> Face alignment -> Embedding -> Recognition
- Python
- OpenCV: Used for image processing and face detection
- FaceNet / ArcFace: Used for converting face in to embeddings (feature vectors)
- NumPy: Used for handling image arrays and embeddings
- Matplotlib: Used for visualization
- scikit-learn