<p>I would try a pair of separate deep image embeddings with a <a href="https://www.tensorflow.org/addons/api_docs/python/tfa/losses/ContrastiveLoss" rel="nofollow noreferrer">contrastive loss</a>. The idea is similar to the <a href="https://en.wikipedia.org/wiki/Siamese_neural_network" rel="nofollow noreferrer">Siamese network</a> architecture. In Siamese networks the pairs of images are of the same type - so both input images are fed through the copy of the same network. In your case the images are of different kinds, so I would just have separate nets for person images and fingerprints.</p>

CC BY-SA 4.0
2021-05-22T22:26:56.123Z
2021-05-22T22:26:56.123Z
Kostya
<p>I have a dataset of two different type of images. Say, I have <strong>images of a person</strong> and <strong>his all 10 fingerprints</strong>. I want to create a relation between them to predict one from another. How I can do that and which architecture is suitable for this problem or similar type of problem.</p>

CC BY-SA 4.0
2021-10-22T11:41:29.400Z
I doubt you can predict a fingerprint image from a person's face, but this is still a valid question for some other datasets.
NikoNyrh
<convolutional-neural-networks><training><deep-neural-networks><neural-architecture-search>
A neural network to learn the connection between two totally different type of images
Prapon
# A neural network to learn the connection between two totally different type of images

(Asked by: username_0 on 2021-05-22)

I have a dataset of two different type of images. Say, I have **images of a person** and **his all 10 fingerprints**. I want to create a relation between them to predict one from another. How I can do that and which architecture is suitable for this problem or similar type of problem.

**Tags:** convolutional-neural-networks, training, deep-neural-networks, neural-architecture-search

> **Comments:**
> **username_1**: I doubt you can predict a fingerprint image from a person's face, but this is still a valid question for some other datasets.  

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## 1 answers

### Answer by username_2 on 2021-05-22. Score: 1

I would try a pair of separate deep image embeddings with a [contrastive loss](https://www.tensorflow.org/addons/api_docs/python/tfa/losses/ContrastiveLoss). The idea is similar to the [Siamese network](https://en.wikipedia.org/wiki/Siamese_neural_network) architecture. In Siamese networks the pairs of images are of the same type - so both input images are fed through the copy of the same network. In your case the images are of different kinds, so I would just have separate nets for person images and fingerprints.


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