How to build your own eye detection classifier algorithm?
I created an eye classifier with 278 self-cut eye-pictures and 31 random background pictures. It detects eyes 96% of the time but also a lot of false positives. How to approach this task?
- Build a stronger classifier, ie more pictures?
- Do that later in software? How?
- ...
I've never trained a classifier, but a 10:1 ration doesn't sound right. What happens if you take more negative samples?
Yes... typically having twice as much negative examples as there are positive ones is a good place to start.