<p>I think this is the questions (and answers) you are looking for are below:</p>

<ul>
<li><a href="https://ai.stackexchange.com/questions/2008/how-can-neural-networks-deal-with-varying-input-sizes">https://ai.stackexchange.com/questions/2008/how-can-neural-networks-deal-with-varying-input-sizes</a></li>
<li><a href="https://stackoverflow.com/questions/1766461/how-are-neural-networks-used-when-the-number-of-inputs-could-be-variable?rq=1">How are neural networks used when the number of inputs could be variable?</a></li>
</ul>

<p>But in your case, at the end you could get rid of the problem by simply using some image embedding. You could take some neural net that is pre-trained for example, <a href="https://arxiv.org/abs/1512.00567" rel="nofollow noreferrer">Inception v3</a> that extracts <code>N</code> features from image, and you will always have constant input, of <code>N</code> features. </p>
Do neural networks accepts other than fixed size of input data?
<p>I am implementing a MultiLayer Perceptron , I am extracting the features of images using SIFT algorithm of image processing and I pass those features to the neural network , the features of images that I am considering are descriptors ,every image has different length of descriptors , some image has 200 descriptors and some image has 240 descriptors , means it's varying . But neural networks accepts fixed size of input data .
How can I pass this type of input to it if it accept varied input then how ?</p>