<p>Multi Layers Perceptron(MLP) can be used for image classification, but it has a lot of deficiency than Convolutional Neural network(CNN). But if you compare <strong>MLP</strong> and <strong>Fisher Faces</strong> , the better one is <strong>MLP</strong>, because Fisher Faces will be increasingly difficult if adding more individuals or classes. You can make a simple MLP model, because it just has 3 layers which are an input layer, hidden layer and output layer, here a source code that you can try:</p>

<ul>
<li><a href="https://towardsdatascience.com/multi-layer-neural-networks-with-sigmoid-function-deep-learning-for-rookies-2-bf464f09eb7f" rel="nofollow noreferrer">https://towardsdatascience.com/multi-layer-neural-networks-with-sigmoid-function-deep-learning-for-rookies-2-bf464f09eb7f</a></li>
</ul>

<p>if you make a model, it will be based on training data, I think if make data trained composed based on noise in background and different light shadows on your image I think it will have a better performance, but remember if you are using <strong>MLP</strong> for image classification it can just predict an image on one spot, for example:
"if you train a model with the object in the middle of an image, your model can not predict it when the image is moved to the different spot".</p>

<p>here is pdf to see Fisher Faces performance:</p>

<ul>
<li><a href="https://core.ac.uk/download/pdf/84801048.pdf" rel="nofollow noreferrer">https://core.ac.uk/download/pdf/84801048.pdf</a></li>
</ul>

CC BY-SA 4.0
2020-04-21T12:58:08.163Z
for some reason yes :) ,but i suggest u to use CNN for image Classification, it have better performance
Firhan maulana rusli
CC BY-SA 4.0
2020-04-21T14:10:49.863Z
I will convert MPL network to C code. That's why I wonder if MPL can be used for image classification. Else I will go with fisher faces.
euraad
CC BY-SA 4.0
2020-04-21T14:15:07.070Z
Try Deeplearing2C on GitHub. I made it.
euraad
CC BY-SA 4.0
2020-04-21T11:19:35.103Z
2020-04-22T14:54:32.800Z
2020-04-22T14:54:32.800Z
Firhan maulana rusli
<p>Depends, if the faces are centered and have the same background yes. You also need a lot of data.</p>

<p>If they are daily life images, then no. You will have very bad generalization.</p>

CC BY-SA 4.0
2020-04-21T14:40:10.927Z
2020-04-21T14:40:10.927Z
FourierFlux
<p>let me try to answer your question. yes, you can use multilayer perceptron to image classification. Multilayer Perceptron is topology
the most common of ANN, where perceptrons are connected to form layers. An MLP has
input layer, at least one
hidden layer, and output layer.
Multilayer perceptron is one method
many used. one of them, regards research on classification
human skin based on its color, Khan
(Khan, Hanbury, Stöttinger, &amp; Bais, 2012)
compare the nine methods for
classifications include BayesNet, J48, Multilayer Perceptron (MLP),
Naive Bayes, Random Forest, and SVM.
The results show that the Multilayer Perceptron (MLP)
produce the highest performance after
Random Forest and J48.</p>

CC BY-SA 4.0
2020-04-21T09:23:03.613Z
2020-04-21T09:23:03.613Z
Muhammad Faiq A.
<p>I'm seeking guidence here.
Can I use Multi Layers Perceptron (MLP), e.g regular flat neural networks, for image classification?</p>

<p>Will they perform better than Fisher Faces?</p>

<p>Is it difficult to do image classification with a MLP network? </p>

<p>It's on basic level like classifying objects and not detailed structures and patterns. </p>

<p>Important to me is that the MLP need to be trained with pictures that can have noise in background and different light shadows.</p>

CC BY-SA 4.0
2020-04-20T02:37:46.773Z
Hi @Daniel, welcome to Artificial Intelligence Stack Exchange, Please [see the tour](https://ai.stackexchange.com/tour) and look around to see how this site works. For your questions, could you please ask one question per post? it will give more focus on each post
malioboro
CC BY-SA 4.0
2020-04-20T05:11:54.547Z
I highly recommend for any image classification you look into convolutional neural networks. These should preform much much better than a standard MLP for image classification. Yes while you can do it with MLP, you probably shouldn't. The main issue comes from the input layer, feeding in each pixel as a giant 1D vector might give you too many parameters to train and an overall inferior model...
PMaynard
CC BY-SA 4.0
2020-04-21T11:12:26.780Z
@PMaynard Thank you. Now I know that MLP is bad practice in image classification. Perhaps I should use Fisherfaces instead?
euraad
CC BY-SA 4.0
2020-04-22T01:30:46.417Z
No use a convolutional neural network, Unless this is an assignment or task that requires it you should almost undoubtedly use a convolutional neural network. Generally for image classification I would say, CNN = Great, MLP = poor/okay, Fisher Faces = Bad.
PMaynard
<classification><image-recognition><multi-label-classification>
Can I do image classification with Multi Layers Perceptron (MLP)?
euraad
# Can I do image classification with Multi Layers Perceptron (MLP)?

(Asked by: username_0 on 2020-04-20)

I'm seeking guidence here.
Can I use Multi Layers Perceptron (MLP), e.g regular flat neural networks, for image classification?

Will they perform better than Fisher Faces?

Is it difficult to do image classification with a MLP network?

It's on basic level like classifying objects and not detailed structures and patterns.

Important to me is that the MLP need to be trained with pictures that can have noise in background and different light shadows.

**Tags:** classification, image-recognition, multi-label-classification

> **Comments:**
> **username_1**: Hi @Daniel, welcome to Artificial Intelligence Stack Exchange, Please [see the tour](https://ai.stackexchange.com/tour) and look around to see how this site works. For your questions, could you please ask one question per post? it will give more focus on each post  
> **username_2**: I highly recommend for any image classification you look into convolutional neural networks. These should preform much much better than a standard MLP for image classification. Yes while you can do it with MLP, you probably shouldn't. The main issue comes from the input layer, feeding in each pixel as a giant 1D vector might give you too many parameters to train and an overall inferior model...  
> **username_0**: @username_2 Thank you. Now I know that MLP is bad practice in image classification. Perhaps I should use Fisherfaces instead?  
> **username_2**: No use a convolutional neural network, Unless this is an assignment or task that requires it you should almost undoubtedly use a convolutional neural network. Generally for image classification I would say, CNN = Great, MLP = poor/okay, Fisher Faces = Bad.  

---

## 3 answers

### Answer by username_3 on 2020-04-21. Score: 2 | Accepted answer

Multi Layers Perceptron(MLP) can be used for image classification, but it has a lot of deficiency than Convolutional Neural network(CNN). But if you compare **MLP** and **Fisher Faces** , the better one is **MLP**, because Fisher Faces will be increasingly difficult if adding more individuals or classes. You can make a simple MLP model, because it just has 3 layers which are an input layer, hidden layer and output layer, here a source code that you can try:

* <https://towardsdatascience.com/multi-layer-neural-networks-with-sigmoid-function-deep-learning-for-rookies-2-bf464f09eb7f>

if you make a model, it will be based on training data, I think if make data trained composed based on noise in background and different light shadows on your image I think it will have a better performance, but remember if you are using **MLP** for image classification it can just predict an image on one spot, for example:
"if you train a model with the object in the middle of an image, your model can not predict it when the image is moved to the different spot".

here is pdf to see Fisher Faces performance:

* <https://core.ac.uk/download/pdf/84801048.pdf>

> **Comments:**
> **username_3**: for some reason yes :) ,but i suggest u to use CNN for image Classification, it have better performance  
> **username_0**: I will convert MPL network to C code. That's why I wonder if MPL can be used for image classification. Else I will go with fisher faces.  
> **username_0**: Try Deeplearing2C on GitHub. I made it.  

---

### Answer by username_4 on 2020-04-21. Score: 1

Depends, if the faces are centered and have the same background yes. You also need a lot of data.

If they are daily life images, then no. You will have very bad generalization.


---

### Answer by username_5 on 2020-04-21. Score: 1

let me try to answer your question. yes, you can use multilayer perceptron to image classification. Multilayer Perceptron is topology
the most common of ANN, where perceptrons are connected to form layers. An MLP has
input layer, at least one
hidden layer, and output layer.
Multilayer perceptron is one method
many used. one of them, regards research on classification
human skin based on its color, Khan
(Khan, Hanbury, Stöttinger, & Bais, 2012)
compare the nine methods for
classifications include BayesNet, J48, Multilayer Perceptron (MLP),
Naive Bayes, Random Forest, and SVM.
The results show that the Multilayer Perceptron (MLP)
produce the highest performance after
Random Forest and J48.


---

