<p>I suppose you can use <a href="https://github.com/AlfredXiangWu/face_verification_experiment" rel="nofollow noreferrer">this model</a>, described in <a href="https://arxiv.org/abs/1511.02683" rel="nofollow noreferrer"><em>Xiang Wu, Ran He, Zhenan Sun, Tieniu Tan</em> <strong>A Light CNN for Deep Face Representation with Noisy Labels</strong> (arXiv 2015)</a> as a a strating point for your experiments.</p>

<p>As for the Siamese network, what you are trying to earn is a mapping from a face image into some high dimensional vector space, in which distances between points reflects (dis)similarity between faces.<br>
To do so, you only need <em>one</em> network that gets a face as an input and produce a high-dim vector as an output.<br>
However, to train this <em>single</em> network using the Siamese approach, you are going to duplicate it: creating two instances of the <em>same</em> net (you need to explicitly link the weights of the two copies). During training you are going to provide pairs of faces to the nets: one to each copy, then the single loss layer on top of the two copies can compare the high-dimensional vectors representing the two faces and compute a loss according to a "same/not same" label associated with this pair.<br>
Hence, you only need the duplication for the training. In test time (<code>'deploy'</code>) you are going to have a <em>single</em> net providing you with a semantically meaningful high dimensional representation of faces.</p>

<p>For a more advance Siamese architecture and loss see <a href="https://stackoverflow.com/q/33330779/1714410">this thread</a>. </p>

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<p>On the other hand, you might want to consider the approach described in <a href="https://arxiv.org/abs/1605.07270" rel="nofollow noreferrer"><em>Oren Tadmor, Yonatan Wexler, Tal Rosenwein, Shai Shalev-Shwartz, Amnon Shashua</em> <strong>Learning a Metric Embedding for Face Recognition using the Multibatch Method</strong> (arXiv 2016)</a>. This approach is more efficient and easy to implement than pair-wise losses over image pairs. </p>
Face Recognition based on Deep Learning (Siamese Architecture)
<p>I want to use pre-trained model for the face identification. I try to use Siamese architecture which requires a few number of images. Could you give me any trained model which I can change for the Siamese architecture? How can I change the network model which I can put two images to find their similarities (I do not want to create image based on the tutorial <a href="https://github.com/NVIDIA/DIGITS/tree/master/examples/siamese" rel="nofollow noreferrer">here</a>)? I only want to use the system for real time application. Do you have any recommendations?</p>