<blockquote>
  <p>Will augmenting data help? Does it improve accuracy?</p>
</blockquote>

<p>That's hard to say in advance. But almost certainly, when you already have a model which is better than random. And when you choose the right augmentation method.</p>

<p>See my masters thesis <a href="https://arxiv.org/pdf/1707.09725.pdf#page=94" rel="nofollow noreferrer">Analysis and Optimization of Convolutional Neural Network Architectures</a>, page 80 for many different augmentation methods.</p>

<blockquote>
  <p>In which case one should choose to augment data and should avoid?</p>
</blockquote>

<ul>
<li>When you don't have enough data -> augment</li>
<li>Avoid augmentations where you can't tell the emotion after the augmentation. So in case of character recognition, rotation is a bad idea (e.g. due to <code>6 vs 9</code> or <code>u vs n</code> or <code>\rightarrow vs \nearrow</code>)</li>
</ul>
Does image augmentation helps?
<p>I am trying to build cnn model (keras) that can classify image based on users emotions. I am having issues with data. I have really small data for training. Will augmenting data help? Does it improve accuracy? In which case one should choose to augment data and should avoid?</p>