I'm working on a project that involves generating a unique ID for a given biometric (such as an iris image). I'm interested in exploring the use of ML techniques for feature extraction and ID generation. Specifically, I'm interested in how various measurements of the biometrics of some person (such as photos of the person’s same iris under different angles) could be almost always converted to the same unique ID.
My main questions are:
- What machine learning techniques could be used to extract the features of a biometric? I'm was planning to work with biometric data in an image format, and I guess that CNNs or deep embedding networks could be useful for this task. Are there any other techniques that could be used (e.g., PCA)?
- What format should the features be represented as? Should they be stored as a vector, a matrix, or some other format?
- How could the features be converted to a an ID? I'm currently considering using techniques such as hashing or one-way encoding. Are there any other techniques that could be used, and what are the pros and cons of each?
- What could be done to ensure that variations of the input biometric would (almost) always lead to the same ID?
I am currently at the research step, thus did not attempt yet to implement such a pipeline. In case you have some questions regarding the project, don’t hesitate to ask, I will try to be more specific if necessary.
I'm also open to any other advice or tips on how to approach a project like this. Thank you in advance for your help!