[theikkila]

I'm little bit troubled by the model created for classifying the vein patterns. With only corpus of size 40 and using two classes without augmentation will most likely end up overfitting the model. I would say the model is currently learning to classify left or right hand but doesn't really care about the veins much.

Have you tried the performance with some other user?

I would also probably use data augmentation and ie. flip and rotate images, vary contrast etc. That might prevent some amount of overfitting.

With these kinds of problems usually classifying models are not very well suited. Basically with neural networks you are causing the manifold to partition whole output space and so you can expect that there is practically unlimited amount of different patterns that look equal to your hand (the class you have trained to be 'your hand')

For better model you need more data, it can be labeled of course but there is also unsupervised options you could consider such as autoencoders. With facial recognition the siamese networks and triplet loss based networks are pretty popular and you could maybe take a look into them.