Say I have a set of data generated by someone. It could be either bytes from a photo, or readings from bio-sensors, and I have a huge amount of said information, from many people or subjects. Which AI algorithms could be used to learn that a set of data belongs to a subject. I would have the information map that a huge set of data belongs to Bob, and another belongs to Alice to train the system.
You have a set of different types of data available for each of your subjects, and given one set you'd like to classify which subject it belongs to. This looks like a supervised classificiation problem.
The most popular classifiers for supervised learning are neural networks. Now given the heterogenous nature of your data types, a simple approach would be to use separate classifiers for each type of data. For example, a convolution neural net for the image data, and a simple feed forward net for the biosensor data. Another thing you try is a multi channel approach, where towards the input side you have multiple channels for the different types of data, and the final few layers are fully connected.
The image has only CNNs for the multi channel part but you could have one channel as a simple feed forward net while another one as having conv layers.
Also, if you wish to classify the data as belonging to a subject on the basis of just one of the data types from the set, then you should have separate classifiers for all types. In that case it might be worthwhile to look into classifiers other than neural nets like multi class logistic regression which might be simpler to work with for a particular data type.