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Artificial Intelligence · Mini Project
Age & Gender Detection
Predicts an age bracket and gender from a detected face
1-2 weeks build
Overview
Faces are detected with OpenCV and passed to two small CNNs trained on the public UTKFace dataset: one classifies an age bracket, the other a gender label. A Streamlit app runs on uploads and webcam, and the report looks honestly at where predictions go wrong, including bias across age groups and why appearance-based gender labels are a limited proxy.
What makes it stand up
- 01Face detection feeding two classification heads
- 02Age brackets instead of a single noisy number
- 03Trained on the public UTKFace dataset
- 04Per-group error analysis to surface bias
- 05Streamlit app for uploads and webcam
Module breakdown
- 01
Face crop
Detection, padding and resizing.
- 02
Age model
CNN classifier over age brackets.
- 03
Gender model
Binary CNN with its limits documented.
- 04
Evaluation
Confusion matrices broken down by group.
- 05
App
Streamlit interface with live predictions.
After this, you will be able to
- Explain why age is framed as classification here
- Discuss dataset bias and ethical limits
- Demo predictions on uploaded photos
Technology stack
- Python
- OpenCV
- TensorFlow
- Streamlit
- Level
- Mini Project
- Domain
- Artificial Intelligence
- Build time
- 1-2 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support