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Then call the shell script from the crontab. Congratulations to you and Trisha! Many of your readers got a chance to meet both of you at PyImageConf, and you make a great couple!

FPS: Hello Adrian, excellent post I want to ask you a question if I follow your course pyimagesearch-gurus or buy the most extensive version of ImageNet Bundle.

I could have support and the necessary information to start a project of face-recognition at a distance for example more than 8 meters.

Hi Francisco, I always do my best to help readers and certainly prioritize customers. Keep up the great work! Thanks Adrian, I know that the effort should be mine, the important thing is to have good bibliography and information, thank you I am very motivated and tis post are of great help especially to developing countries like in which I live.

I want to use this face recognition method in form of a mobile application. Yes, but make sure your data augmentation is realistic of how a face would look.

Congratulations Adrian, thank you for the tutorial. I am starting to follow you more regularly. I am amazed with the detail in your blogs.

I am just curious how long each of these tutorial takes you to plan and author. Thanks Neleesh. As far as how long it takes to create each tutorial, it really depends.

Some tutorials take less than half a day. Others are larger, on-going projects that can span days to weeks. This tutorial actually covers how to build your own face recognition system on your own dataset.

Just refer to the directory structure I provided and insert your own images. Adrian, Congratulations on your marriage!

Take some time off for your honeymoon and enjoy the best time of your life! I do not have any liveliness detection tutorials but I will try to cover the topic in the future.

I wonder if Adrian or anyone else has actually combined the dlib landmarks with the training described in this post? It seems to require additional steps which are not that easy to infer.

When I changed up the model I saw that it basically only recognized the first name in the dict that is created and then matches every found face to that name in one case it even matched a backpack.

I spotted a difference between the dicts that get pickled. Maybe this is a problem cause? Another small difference is that this post uses embeddings in its code and the previous one calls them encodings.

We are trying to run the code off an Nvidia Jetson TX2 with a 2. Is there any way to resolve these problems?

No, face recognition and liveliness detection are two separate subjects. You would need a dedicated liveliness detector.

First of all thanks for the tutorial. You would replace use the model from dlib face recognition tutorial instead of the OpenCV face embedder.

Just swap out the models and relevant code. Give it a try! Hi Andrian, your posts are always inspiring. Simply replace the caffemodel file seems not work.

How should I rewrite the code? PS: Congratulations on your marriage! Thanks again Zong. Hey Zong — which SqueezeNet model are you using?

Having attempted the 1st few sections of your post recognize. Yes, I read further down the post that more datasets will eventually lead to much-needed accuracy.

Look forward to your feedback. I have a question on this. What if, I already have pre-trained model for face recognition say FaceNet and on top of it I want to train the same model for a few more faces.

Is it possible to retrain the same model by updating the weights file. I have tested your code for a week. But when I increased number of people upto 10 , it looked unstable sometims.

In my test, sometimes, face naming was too fluctuated, I mean, real name and other name was switched too frequently. After that, face naming seemed to get more stable, but there are still fluctuated output or wrong naming output frequenty.

Is there any method to increase accuracy? Is there possibility on a relation-formula of between face landmark points to distinguish each face more accurately?

I tried ti find ,but I still failed. Once you start getting more and more people in your dataset this method will start to fail. Try instead fine-tuning the network itself on the people you want to recognize to increase accuracy.

The models covered in this post will give you better accuracy. I wish to know do you follow any algorithms, kindly mention, if any?

I can see this stream in vlc on any computer on my network, so i should be able to use that as the source in your script.

Second, instead of viewing the results on my screen, how can I can Output it in a format so I can watch it from another computer.

Example, How can I create a stream that I can feed into a vlc server, so I can watch it from another computer on my network.

If you need help actually building the face dataset itself, refer to this tutorial. You are so kind and generous…you must be an amazing human being.

Thank you for this tutorial. The results are entirely dependent on the algorithm and the camera itself.

This code i ran in ubuntu. But in my Mac everything was fine. I used the same version python and opencv. Thank you. The path to your input images does not exist on disk.

Double-check your images and paths. Hendrick, I had the same error but it was a problem with the webcam under Ubuntu.

Once I set that up correctly everything worked fine. Hi Adrian. The scikit-learn documentation has an excellent example of plotting the decision boundaries from the SVM.

Re-train your face recognition model and serialize it to disk. LabelEncoder seems to be reversing the labels. If you try to print knownNames and le.

So when you call le. It seems to be causing misidentification on my datasets. This happens when the list of images are not sorted.

After adding sorting of the list of dataset images, it works without problem. By the way, linear SVM seems to perform bad with few dataset images per person.

Using other classification algorithms such as Naive Bayes are better suited few datasets. Is it possible to represent the name in other languages, i.

Thank you very much! You can use whatever names in whatever languages you wish, provided Python and OpenCV can handle the character set.

Many thanks for your tutorials. Step by step following your instruction, I have successfully implemented 7 tutorials on my RPi.

The most fun part is this opencv face recognition tutorial. I train the model by adding my family members. It works pretty accurate at most time but sometimes either your name or your wife name pops up.

LOL Anyway, your professional tutorial makes me feel like a real coder, though I am actually a dummy :. I tried to run this project using opencv 3.

I would highly recommend you use OpenCV 3. You can actually install OpenCV via pip and save yourself quite a bit of time. BTW, you had in one of your articles mentioned a link to the zip file containing the General Purpose Faces to be used with the code.

Can you please share that link once again over here? Hi Adrian, Thanks for the great tutorial and clear site. Its a ton of information. I just started this afternoon after searching the web on how to start, and now i have my own small dataset, and the application is running great.

I am facing this error when I run train model: ValueError: The number of classes has to be greater than one; got 1 class.

Are you trying to train a face recognizer to recognize just a single person? Keep in mind that you need at least two classes to train a machine learning model.

What happens if you do want to just train one one person, at least for the time being? There may eventually be more than one person, after more people sign up, but for the first user there would only be one person.

Good luck! One of the requirements of the teacher is the installation of the scikit-learn package.. Now, my concern is, my teacher also expressed that people that use PyTorch or TensorFlow will get a better grade in their projects.

In that case, can scikit learning and PyTorch work together? Am i misunderstanding something about this? Also, what possibly could i add in terms of PyTorch usage that could improve this tutorial that you provided besides the points that you mention in the end of the tutorial face-aligment, more data, etc?

I personally prefer Keras as my deep learning library of choice. I see, so in this tutorial in particular we are indeed using PyTorch and scikit together, correct?

No, this tutorial is using OpenCV and scikit-learn. The model itself was trained with PyTorch there is no actual PyTorch code being utilized.

Instead, we are using a model that has already been trained. I found This technique is not gives output accurately.. Yes I followed your Suggestions.

I take 70 samples per person. How many unique people are in your database? Adrian i include 3 peoples in my dataset.

For only 3 people the model should be performing better. Have you used the dlib face recognizer as well?

Does that model perform any better? At that point if dlib and the FaceNet model are not achieving good accuracy you may need to consider fine-tuning an existing model.

But for only 3 people either dlib or FaceNet should be performing much better. I think there may be a logic error in your code so I would go back and reinvestigate.

If so, how? Take a look at my face alignment tutorial on how to properly align faces. You would want to align them before computing the d face embeddings.

High resolution images may look visually appealing to us but they do little to increase the accuracy of computer vision systems.

We reduce image size to 1 reduce noise and thereby increase accuracy and 2 ensure our algorithms run faster. The smaller an image is, the less data there is to process and the faster the algorithm will run.

Dimensionality reduction typically refers to a set of algorithms that reduce the dimensionality of an input set of features based on some sort algorithm that maximizes feature importance PCA is a good example.

Hello Adrian, how we could train a model to recognize rotated faces in different angles??? I want to make facial recognition through a eye fish camera.

You would detect the face and then perform face alignment before performing face recognition. Hi Adrian, if I previously have many images trained using the SVM, and now I have several additional images correspond to new people , I need to retrain the SVM by scanning through all d vectors.

It would take a lot of time when the number of images is kept increasing. You are correct, you would need to re-train the SVM from scratch.

Apart from the scalability issue, I would like to know the performance of SVM compared with other simple classifier. For example, L1, L2 distance, and cosine similarity.

Any comments on this comparison? Are you asking me to run the comparison for you? This blog is here for you to learn from, to get value from, and better yourself as a deep learning and computer vision practitioner.

I would highly encourage you to run the experiments and note the results. Let the empirical results guide you. Hello Adrian, Congratulations for your wedding!

I was going through your code. When I ran it, the faces which were there in the model were detected accurately. But the faces which were not there were detected wrongly as some one else.

I had about images of each person. Any idea on how I can reduce the false positives? See this tutorial for my suggestions on how to improve your face recognition model.

See this tutorial on face clustering. Applying on that the extraction of embedding I noticed something was wrong because not all the images were processed.

To confirm that I also just modified the routine cropping the ROI for each image from the face detection without performing alignment and saving it as new dataset and the extraction step just serialized 1 encoding!!!!

Could you pls help??????????? You performed face detection, aligned the faces, and saved the ROI of the face to disk, correct?

From there all you need to do is train your model on the aligned ROIs not the original images. If only 1 encoding is being computed then you likely have a bug in your code such as the same filename is being used for each ROI and the files are overwriting each other.

You may have a path-related issues as well. Yes, but the face recognition will be very slow. You may also need to use a Haar cascade instead of a deep learning-based face detector.

Muhammad, I have a raspberry pi and a camera located where I want to capture images and then the images are sent back to my main PC for processing.

Both of your questions can be address in this tutorial. Hello Adrian, firstly, I am grateful for your work.

It has helped me for my Senior Design class project. I want to ask you a question: The way machine learning algorithms usually work from what I understand is, it gets trained on dataset allowing the algorithm to set weights.

When training is done and we want to predict or classify we simply input the new data into a function which already has weights set. Effectively we do not have to compare the new data to all the previous data.

Now, the algorithm for face recognition you described has to look for a face at each frame and then encode it and then compare it to every single encoding in the database.

While this is fine for my project since we are only 3 in the group and each has about 50 images in their face directories, it is relatively slow.

However, is there a way of training the machine in such a way that instead of going through each individual encodings in my case it can go through only 3 where each encoding is going to be some kind of average of one persons face.

I know doing the avarage is kind of silly coz of angles and facial expressions etc. We have a pre-trained face recognizer that is capable of producing d embeddings.

The model will still need to perform a forward-pass to compute the d embeddings. That said, if you want to train your own custom network refer to the documentation I have provided in the tutorial as well as the comments.

Perhaps, I did not phrase it correctly. Finding a face on each frame is very similar to what other machine learning algorithms do.

What I was asking about is comparing the already embedded face to each and every face encoding in the database.

To be precise, the efficiency of the voting system is under the question. I was wondering if it is possible to compare the encoded face from frame to some kind of average encoding of each person in the database.

It would be easier to instead perform face alignment , average all faces in the database, and then compute the d embedding for the face.

Hi Adrian! Thank you so much for your work. Is there a way to add images of new people to an already trained system without running through all already existing images?

Yes, you can insert logic in the code to check and see if a face has already been quantified by the model the file path would serve as a good image ID.

If so, skip the image but still keep the computed d embedding for the face. The actual model will need to be retrained after extracting features.

Hello Adrian, can u please tell me why u passing unknown person images, this model itself should recognize unknown person if it not trained on that person….

You can use an SVM with a linear kernel to obtain your goal. No, you should use a different type of machine learning or deep learning than that.

Why linear svm classifier is better than knn classifier? Which method is most effective when we have dataset and many faces?

Hey Sari — I cover machine learning concepts this tutorial. That post will help address your question. Hi adrian, I am not satisfied with the SVM trained model, can i define my own deepLearning network using tensorflow instead of svm to get better result?

Have you tried fine-tuning the existing face embedding model? I am using openface the same embedder model, how to make tuning ,please tell me. Hi Adrain, I am working for Face recognition feature implementation for Robot to recognize registered office members face.

With these few samples, we will need to do the face recognition. May be my team members are chinese and look similar? So, here i need your advise and suggestion on which one to use?

Or your previous post with dlib? Please suggest. But after this training model script, i see still the face recognition is not so accurate as expected for Robot.

Please correct me if i did anything wrong here. This one. Thanks a lot for such an informative post. I have followed the procedure to train my own set of images and recognize.

My question is if the network cannot work effectively for the new set of images, how does it classify you or trisha for just 6 images? I have done this project, and done it using webcam.

Now when the frame window is opening it is giving an fps of 0. Due to this we are not getting accurate output. So please do tell us how to resolve this issue.

Is this the problem of webcam or raspberry pi? I have to use Deep learning classifiers instead of linear support vector classifier …how it can be done?

Adrian , SVM is not satisfactory … could pls refer me a deep learning model to train on the embeddings…for better accuracy… and if any new face is detected it is not recognizing as unknown….

Hi Adrian, I was wondering whether the dlib pipeline which you wrote in another post, takes care of face alignment or do we have to incorporate it?

No, you need to manually perform face alignment yourself. Refer to this tutorial on face alignment. I have addressed that comment in the comments section a few times, please give the comments a read.

Kindly take the time to read the tutorial. Hi Adrian.. Thank you so much for this guide! Thanks in advance! If again same that unknown person will come,It have to show previous generated Id.

Hi, Adrian I am a fan of your blog. Your blog had really helped me learn OpenCV a lot. While in this tutorial OpenFace is used for face detection and SVM is used for face recognition and classification.

My question is if I used this method, will the false positive still occurs if I will need to recognize the , of people? For 1,, people you should really consider fine-tuning the model rather than using the pre-trained model for embeddings.

You will likely obtain far better accuracy. Thanks for your reply Dr Adrian, what does fine-tuning the model means? Does it mean we need to retrain the K-NN or SVM model for the classification process or we need to retrain a custom model for face detection?

Because it seems like dlib doing a good job detect face inside the image. This post covers fine-tuning in the context of object detection — the same applies to face recognition as well.

Thanks Dr Adrian. I will check on your post. It sounds like the path to your input directory of images is not correct. Double-check your file paths.

Thanks for such awesome blogs and I really learnt many concepts from you. You are kind of my Guru in computer vision. I needed a little help, I am trying to combine face recognition and object detection both in single unit to preform detection on single video stream.

How I am suppose to load 2 different model to process video in single frame? Kindly help. I would suggest you take a look at Raspberry Pi for Computer Vision where I cover object detection including video streams in detail.

I downloaded the code and made sure all the dependencies and libraries were installed. Unfortunately, whenever i run the code it works for the first couple of seconds identifying faces perfectly, then after a few seconds it causes the PC to crash resulting in a hard reboot.

Double-check the path to your input file. You published many face recognition methods, which one would you consider the most accurate? It depends on the project but I like using the dlib face recognition embeddings and then training a SVM or Logistic Regression model on top of the embeddings.

I found that overall people have problems with importing deep learning models into cv. How such architecture will differ in terms of speed compared to the case when open cv uses a pretrained model as you showed above.

You can technically use a microservice but that increases overhead due to HTTP requests, latency, etc. Hello Adrian, when i download and use your trains and code without changing anything with adrian.

There are like squares adrians. I gave it a try with my photos, added like 40 photos, removed outputs. The fact that there are multiple face detections which is the root of the issue.

What version of OpenCV are you using? Hello Adrian, i use OpenCV 4. I would suggest taking a step back. Start with a fresh project and apply just face detection and see if you are able to replicate the error.

I ran your code successfully. However, in some cases, I want to filter the images with lower confidence. For example, the code recognizes two people as me with the confidence Check the confidence and throw out the ones that are Dear adrian, first thank you for your excellent tutorial it is very helpful, I am PhD student in computer science, I saw your tutorial about facial recognition, I was very interested in your solution, and i want to know if it is possible to make the search on web application From web Navigator instead of using shell commande, thnak you very much.

Yes, absolutely. This tutorial would likely be a good start for you. Hey Adrian, I know its been a while since you answered a question on this post, but I have one lingering curiosity.

I have been trying to add members of my own family to the dataset so it can recognize them. I regularly comment and help readers out on this post on a weekly basis.

Extract the facial embeddings from your dataset 3. Train the model. You can read about command line arguments in this tutorial. You can use them to perform face alignment.

I was wondering how to recognize multiple faces. Could you give me some leads on that? And thank-you for all your great tutorials and codes.

Thanks once again. I just have a question, each time you add a new person, do need to train again the SVM or exists another way?

I just have one question. It will already do this. Each image gets converted into an embedding a bunch of numbers. Each person will have a pattern to their embeddings.

If you have enough images, the SVM will pick up on those patterns. Hi adrian!! I am a big fan of your work and although it is too late i wish you a happy married life.

I was wondering , can we combine your open cv with face recognition tutorial this tutorial with the pan-tilt motor based face recognition tutorial and enhance the fps with movidius ncs2 tutorial on raspberry pi to make a really fast people identification raspberry pi system which can then be utilized for further projects.

I just wanted to know whether it can be done or not and if it can be done, how should i go ahead with it? I have already applied and made these projects separately in different virtual environments, now i need to somehow integrate it.

Thanks for your help in advance. For my case at least, the issue was that I am doing the tutorials on a Linux machine but I collected the images using my Mac and then copied the folders across the network to the Linux machine.

That process copies both the resource and data forks of the image files on the Mac as well as the Mac. Many of these files are hidden.

Once I made fresh dataset image folders and copied the training images into them using the Linux machine, all was good.

That exact question is covered inside Raspberry Pi for Computer Vision. I am using it on Windows machine, it worked great.

Thank you once again for creating it. U can help me to assign the picamera to on Jetson Nano for videostream face recognition?

Hello Adrian! Thanks a lot for these tutorials. Your tutorials have been my first intro to Computer Vision and I have fallen in love with the subject!

How well does SVM scale? I tried to do a test with dummy vectors, and the training time seems to scale exponentially. Have you had any experiences in scaling this for large datasets in the order of tens of thousands of classes perhaps?

Also, what is your opinion on using Neural Networks for the classification of the embeddings as opposed to k-nn perhaps with LSH or SVM for scalability?

Thank you once again for these wonderful tutorials! Hey Adrian. Thank you for this amazing tutorial.

Loved it. Like people approaching my front door or maybe people in a locality , given I have the dataset of that locality. Can you please help me on this.

How can I use this tutorial in doing that. That exact project is covered inside Raspberry Pi for Computer Vision. I suggest you start there.

Great post as usual but wondering why SVM is used for classifying rather than a fully connected neural network with softmax activation?

You could create your own separate FC network but it would require more work and additional parameter tuning. Very useful, informative, educational and well presented in layman terms.

I have learnt a few things so far thru your articles. How would I know that? Was hoping to hear your opinion on it. I need to be able to identify that so that I can train my engine with a better set of photos.

Hi Adrian, thanks for the tutorial. I have a question about processing speed. Is there any way that the forward function speed can be improved or why does this take the most time?

When running this on a Raspberry Pi, it seems to be the bottleneck of the recognition. Makes things especially harder when trying to recognize faces in frames from a live video stream.

What seems to be the problem? Doch User des russischen, 4chan -ähnlichen Forums Dvach missbrauchten die App, um mit der Gesichtserkennung Darstellerinnen in Pornovideos auf VKontakte ausfindig zu machen.

Sechs gesuchte Personen hätten dank der Technologie festgenommen werden können, sagte ein Regierungssprecher dem Tech-Portal The Verge.

Algorithmen stufen Menschen anhand von Fotos als schwul ein, und Apples neues Handy scannt Gesichter: Die Technik der Gesichtserkennung macht viele nervös.

Doch in der Debatte geht einiges durcheinander. Von Jannis Brühl und Hakan Tanriverdi. Damit sollen Darstellerinnen in Videos automatisch identifiziert und die Clips entsprechend verschlagwortet werden.

Fabian Thylmann im Interview. Biometrie und Gesichtserkennung. Lesen Sie mehr zum Thema Datenschutz. Zur SZ-Startseite.

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