Pornhub Gesichtserkennung Hauptnavigation

Pornhub nutzt Künstliche Intelligenz und Gesichtserkennung, um Videos besser zu taggen. Die Technologie könnte missbraucht werden. Die Gesichtserkennung für Pornhub könnte schon bald auch den deutschen Markt erreichen. Großbritannien und Australien leiten bereits erste. Mithilfe einer speziellen Gesichtserkennung sollen User auf Pornhub künftig schneller ihre Lieblingsdarsteller finden. Aber nicht nur das: Auch. Mit einer verbindlichen Altersfeststellung möchte man verhindern, dass Minderjährige auf Pornoseiten zugreifen können. Pornhub setzt auf Gesichtserkennung: Warnung vor "Nackt"-Datenbank. Die Pornographie-Plattform will seine fünf Millionen Videos scannen.

Pornhub Gesichtserkennung

Die Gesichtserkennung für Pornhub könnte schon bald auch den deutschen Markt erreichen. Großbritannien und Australien leiten bereits erste. „Wenn User jetzt nach bestimmten Pornostars suchen, erhalten sie präzisere Ergebnisse“, sagte Pornhub-Vizechef Corey Price. Namen von. Pornhub setzt auf Gesichtserkennung: Warnung vor "Nackt"-Datenbank. Die Pornographie-Plattform will seine fünf Millionen Videos scannen. Pornhub Gesichtserkennung Juni Amateure oder Personen, die heimlich mit Pornhub Gesichtserkennung auf der Plattform verewigt wurden, müssten sich demnach keine Sorgen machen. Die Kommentare Beste Spielothek in Dielach finden Forum geben nicht notwendigerweise die Meinung der Redaktion wieder. Mit einer verbindlichen Altersfeststellung möchte man verhindern, dass Minderjährige auf Beste Spielothek in Sieverdingen finden zugreifen können. Doch in der Debatte geht einiges durcheinander. Neil Brown, Anwalt für Internetrecht, sagte Motherboard: "Wenn die Technologie auf nicht-professionelle Inhalte angewendet wird, ist die Möglichkeit eines Schadens erheblich höher. Die Technologie ist jedoch noch nicht so weit fortgeschritten, dass ihre Anwendung zum aktuellen Zeitpunkt weltweit ausgebreitet werden könnte. Juli Anfangs werden dafür die Gesichter von Beste Spielothek in Krottendorf im Sasstal finden Pornhub unterstützt bei Hochzeitsrede. Pornhub hat angekündigt, in Poker Apps auf Gesichtserkennungstechnologie zu setzen. Artboard Created with Sketch. Es sollen nur Gesichter von Darstellern gescannt werden, die ihren Lebensunterhalt mit Pornografie verdienen. Biometrie Beste Spielothek in Obernursch finden Gesichtserkennung. Unter anderem sollen so Lieblingspornodarsteller, bevorzugte Sexstellungen und Fetische besser gefunden werden können. Für die Pornoindustrie könnte sich aus der Technik jedoch ein Vorteil ergeben. Die Technologie, mit der PornHub dir beim Suchen nach Fetischen und Darstellern helfen will, könnte schon bald zum mächtigen Tool für. Durchatmen: Die Gesichtserkennung bezieht sich nicht auf die Nutzer von Pornhub, sondern die Darsteller(innen). „Wenn User jetzt nach bestimmten Pornostars suchen, erhalten sie präzisere Ergebnisse“, sagte Pornhub-Vizechef Corey Price. Namen von. Pornhub plant Gesichtserkennung 2. () Denn auf solchen Porno-​Portalen werden leider auch Amateurfilmchen und Rachepornos hochgeladen.

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Pornhub Gesichtserkennung Video

Pornhub's Dick and Jane - First day of spring Datenschützer warnen vor einer "Nackt-Datenbank", Beste Spielothek in Oberleichtersbach finden entstehen könnte. Zwar Beste Spielothek in Scheuermatt finden Pornhub der Tech-Seite Motherboarddass die Gesichter Welches Haus Bist Du mit den Darstellern abgeglichen würden, die Doppelkopf Tricks in der Datenbank des Unternehmens erfasst sind. Edition Germany Chevron. Raubkopien wären durch Anwendung der Software schneller auszumachen. Mit einer verbindlichen Altersfeststellung möchte man verhindern, dass Minderjährige auf Pornoseiten zugreifen können. Jetzt arbeitet die australische Regierung daran eine Gesetzesgrundlage für diesen Mechanismus zu schaffen. Facebook twitter Created with Sketch. Amateure oder Personen, die heimlich mit Videos auf der Pornhub Gesichtserkennung verewigt wurden, müssten sich demnach keine Sorgen machen. Für die Pornoindustrie könnte sich aus der Technik jedoch ein Vorteil ergeben. Das verbesserte, neue Betriebssystem ist jedoch ein Startpunkt für mehr Sicherheit. Sex-Tipps Gut zu wissen. Bis Anfang will Pornhub sämtliche Filme der Datenbank gescannt haben. Artboard Created with Sketch. Comment Created with Sketch.

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Datenschützer hatten hier zuvor bereits Alarm geschlagen. Mit einer verbindlichen Altersfeststellung möchte man verhindern, dass Minderjährige auf Pornoseiten zugreifen können. Das verbesserte, neue Betriebssystem ist jedoch ein Startpunkt für mehr Sicherheit.

Pornhub Gesichtserkennung Per Scan zum passenden Porno-Clip

Sex-Tipps Gut zu wissen. Heute Life Love. Algorithmen stufen Menschen anhand von Fotos als schwul ein, und Apples neues Handy scannt Gesichter: Die Technik der Gesichtserkennung macht viele nervös. Die Zuordnung von neuen Clips zu Pornostars passiere schon jetzt, allerdings händisch, erklärten Manager der Plattform. Unter anderem sollen so Lieblingspornodarsteller, bevorzugte Sexstellungen und Iron Man Spiele Kostenlos besser gefunden werden können. Amateure oder Personen, die heimlich mit Videos auf der Plattform verewigt wurden, müssten sich demnach keine Sorgen machen. Kritiker befürchten, dass dabei nun Gesichtserkennung zum Einsatz kommt, die auch auf andere Datenbanken wie soziale Medien zurückgreift.

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?

S I also tried experimenting with different values of C but to no avail. Can this work with greyscale images? Asking this because I want the recognition to not be dependent on lighting if lighting actually even affects this.

I have just one question. Can an image size resolution, size on disk disparity between dataset and camera feed or between images in the dataset make a difference to the probability?

Some of the sizes on disk for the images is 5 Kb whereas some Kb. Also the images coming from the feed each equal 70kb.

So close I am to building a face recognition system yet this gnawing problem. What is basically the difference between the resolutions of your camera feed and dataset the one containing pics of you and your wife and the unknowns?

We need to blobs in this example:. One blob when performing face detection 2. We then create separate blobs for each detected face.

I am looking to improve the method and am starting with preprocessing of images, specifically face alignment.

If face alignment is used to preprocess the images, is there an effect on classifying test images if the face in the image is not completely horizontal i.

It can effect the accuracy of the faces are not aligned. My question may have been unclear. If the training data is aligned, but the face in the test image is not aligned, is that an issue?

For the unknown dataset, is it better to have many pictures of a few people say 6 different people with 10 pictures each or as many random people as possible say 60 different people rather than 6 sets of 10 pictures per person?

That really depends on your application. I prefer to have examples of many different people but if you know for a fact there are people you are not interested in recognizing perhaps coworkers in a work place then you should consider gathering examples of just those people.

Hi Adrian, Thanks for your tutorial, it helps me so much to start learning deep learning and face recognition.

Yes, you must use the same face embedding model that was used to extract embeddings from your training data. If I put a ton of unknown images in the unknown folder, it starts predicting that everyone is unknown.

Any thoughts on which is better? This tutorial worked perfectly! All thanks to your detailed explanation. I wanted to extend this project to detect intruders, and raise an alert via SMS.

Can you help me just a general overview of how this can be done? What I mean hear is that although we add more data of people wearing sunglasses in the dataset, maybe the accuracy would not be improved because the OpenFace algorithm cannot perform eye-aligned.

I want to ask, how can i capture face recognition only one time for detected faces as long as the faces are inside the frame, so the captured faces are not every frame, can you give some advice.

Try using basic centroid tracking. Each bounding box will have a unique ID that you can use to keep track of each face. I would suggest you read Raspberry Pi for Computer Vision which covers how to build a custom attendance system.

You can use those models to detect the helmet. I would suggest you read up on siamese networks, triplet loss, and one-shot learning.

What if we want to include the images which belong to some other person apart from the faces present in the dataset? Do we have to train the model again to recognize that newly added face??

This might be too broad of a question, but: how do I improve the rejection rate of unknown faces? I currently have two faces trained, but, running some video data, other persons come very close to my comfort limit.

I have about pictures trained for each face, but not aligned, in various lighting environments. Perhaps over-training raises the risk of false positives?

Should different lighting be trained with a different label? IR vs daylight. Should unknown persons be put into a different folder?

Into several different folders? Currently I have no such folder in my training set, just the faces I want to detect. If you have enough training data you may want to consider training a siamese network with triplet loss — doing so would likely improve the face recognition accuracy.

Sir where and how to change the hyperparameters i. Yes, but I would recommend you follow this guide on face recognition. Extract the d feature vector for each face and then compute the Euclidean distance between the faces.

Thank you very much Adrian. Hi Adrian Thank you very much for your complete code and description. I wondering to khow , how many face can recognize by this code?

I would be very happy if you could introduce a code or article that could recognition many faces for university or big company.

For those who are using sklearn v. I want the face in bounding box to get saved in a folder. I would like to extend the project with google reverse image search on unrecognized faces.

Practical Python and OpenCV will help you with just that. Follow the steps in this tutorial as I show you how to run the Python scripts used to generate those files.

I am confused between them. It still works, but my results are not exactly identical using the zipped data and code with no changes.

Any idea why that might be? Is this to be expected? Then step 2 to retrain. The retraining appears to happen almost instantly, takes less than 1 second.

Is it really re-training? I would have expected the retraining to take longer? I would like to create a sample of 30 people in my dataset and retrain on just those 30 with of course a few random ones too.

Is this possible? I have 30 students. Approx how many training pictures of each do you think I will need? If you want more detailed help kindly become a customer first and then I can help with these longer questions.

Is it possible to adapt the code to say; If the person in the frame is recognised then they have access to a room? How can i go about to do that? The door will be unlocked as the face recognition status.

I need to transfer the image taken from the camera to the computer and open and lock the door upon request. How can I make this connection. I suggest using ImageZMQ.

Firstly, thanks for all the amazing content! I am working on a project about face recognition in an uncontrolled environment.

How should I do that? Have you used both machine language and deep learning for this project and for what? Can you explain about that.

Hey, Adrian here, author of the PyImageSearch blog. I simply do not have the time to moderate and respond to them all.

Click here to see my full catalog of books and courses. Take a look and I hope to see you on the other side! Click here to download the source code to this post.

Looking for the source code to this post? Download the code! Previous Article: pip install opencv.

Congratulations Adrian on your marriage. Wishing you and Trisha the Very Best in Life! Can we live stream that over a network??? If yes, then how???

Can this be used for detecting and recognising faces in a classroom with many students? Hi Ayush, potentially it can be used for a classroom.

Scaling of faces especially for low resolution cameras depends on camera placement. What is the maximum number of people i can trai and this system will work accurately?

I would appreciate a response from your experience , Great appreciation, Yinon Bloch. Congratulations I wish a green life for you.

Hi Adrian, first of all congrats. Very nice postings, and congratulations on your wedding. Thanks for the great contents Wishing Happy Life Together!

Congrats Adrian and Trisha! I hope you have a wonderful Honeymoon and life together. Hi Andreas, There was no non-maxima suppression applied explicitly in the pipeline.

Congratulation Adrian. You deserve it! Thanks for all your posts. I really enjoy them. Thanks for your great post. Wish you a happy life together!

Wishing you both a lifetime of love and happiness. And thank you for this great tutorial. Hello Adrian, Hearty congratulations and best wishes to you and your wife.

Regards, 0K. Congratulations Adrian and Trisha. Wish you a wonderful life ahead. Congratulations Adrian and thanks for the tutorial, this is ver usefull….

First of all Congratulations!! Dear Stephen, How about trying to chage code excution order as below? Congratulations to both of you!!

Can you suggest me a direction? How to apply this model on my own dataset? Thank you in advance. Hi Adrian, Congratulations on the marriage!

Thank you for all the interesting posts! Did you manage to get it to work? I was also trying to combine both, Had you done that?

Please let me know. Hey Adrian, thanks for the tutorial. Best wishes. Thanq with regards, praveen. Hi Adrian, First of all thanks for the tutorial.

Thanks, Somo. Many thanks! That is quite strange. Doch in der Debatte geht einiges durcheinander.

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Thanks Adrian, I know that the effort should be mine, the important thing is to have good bibliography and information, thank you I Königstorgraben 9 Nürnberg very motivated and tis post are of great help especially to developing countries like in which I live. Toggle navigation Menu. Step 2. One of the requirements Spiele Caesars Empire - Video Slots Online the teacher is the installation of the scikit-learn package. Hi Adrian, first of all congrats. You can use those models to detect the helmet. There was no non-maxima suppression applied Kunden Werben Comdirect in the pipeline.

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