I'm relatively new to image classification. Currently, I am trying to classify insect images, using a convolutional neural network (CNN). When I ask a human expert to identify an insect, I usually provide 2 photos: back and face. It seems that sometimes one feature stands out and allows identification with high certainty ("spots on the back - definitely a ladybug"), while other times you need to cross-reference both angles ("grey back could mean a few things, but after cross-referencing with the eyes - it's a moth").
How is it customary to implement this? Naively I was considering:
Two separate networks, one for backs and one for faces? If so, what formula is best for weighing in their outputs?
Single network, but separate dual classifications - e.g. "moth face", "moth back", "ladybug face", "ladybug back"?
A single network, feed everything naively (e.g. moths from different angles, all with the same classification "moth") and rely on the NN to sort it out itself?