Splitting binary classification into smaller susbsets
If you try to get the best accuracy, etc... for a given question you should always learn on a training set that is labeled exactly according to your questions. You shouldn't expect to get better results if you are using more granular class labels. The classifier then would then try to pick up the differences in the classes and try to separate them apart. Since in practice your variables in the training set will not perfectly explain the more granular classification question you shouldn't expect to get a better answer for your less granular classification problem.

If you are not happy with the accuracy of your model you try the following instead:


review the explanatory variables. Think about what might influence the classification problem. Maybe there us a clever way to construct new variables (from your existing ones) that helps. It's nowpossible to give a general advise on that since you have to consider the properties of your classifier
if your class distribution is very skewed you might consider over/undersampling 
you might run more different classifiers and then classify based on the majority vote. Note that you will most likely sacrifice explainability of your model.


Also you seem to have some missunderstanding, when you write 'you would assign it to human if it doesn't fall into any of the granular classes'. Note that you always try to pick class labels covering the whole universe (all possible classes). This can be always defined as the complement of the other classes. Also you will have to have instances for each class in your training set.