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Face Applications Raspberry Pi Tutorials. But as I hinted at in the post, in order to perform face recognition on the Raspberry Pi you first need to consider a few optimizations — otherwise, the face recognition pipeline would fall flat on its face.

To learn more about using the Raspberry Pi for face recognition, just follow along. You can follow my instructions linked on this OpenCV Install Tutorials page for the most up to date instructions.

On your Pi, you should unzip the archive, change working directory, and take a look at the project structure just as I have done below:. Before we can apply face recognition we first need to gather our dataset of example images we want to recognize. We will be using a deep neural network to compute a d vector i. First, we need to import required packages. From there, we handle our command line arguments with argparse :. Otherwise, use the hog face detection method.

Line 56 constructs a dictionary with two keys — “encodings” and “names”. The values associated with the keys contain the encodings and names themselves. The data dictionary is then written to disk on Lines Mine is named encodings. No problem, just use the –detection-method hog command line argument. The rest of the modules listed are part of your Python installation.

We proceed to grab a frame and preprocess it. The preprocessing steps include resizing followed by converting to grayscale and rgb Lines Well, he was referring to growing dinosaurs. As far as face recognition, we can and we should detect and recognize faces with our Raspberry Pi.

Haar cascades are also known as the Viola-Jones algorithm from their paper published in The highly cited paper proposed their method to detect objects in images at multiple scales in realtime. For it was a huge discovery and share of knowledge — Haar cascades are still well known today.

Parameters to the detectMultiScale method include:. The result of our face detection is rects , a list of face bounding box rectangles which correspond to the face locations in the frame Lines 47 and We convert and reorder the coordinates of this list on Line We then compute the d encodings for each face on Line 56 , thus quantifying the face. From there, we simply draw rectangles surrounding each face along with the predicted name of the person:. We also update our fps counter.

Our face recognition pipeline is running at approximately FPS. The vast majority of the computation is happening when a face is being recognized , not when it is being detected.

Furthermore, the more faces in the dataset, the more comparisons are made for the voting process, resulting in slower facial recognition. Therefore, you should consider computing the full face recognition i.

We will be covering object tracking algorithms, including centroid tracking, in a future blog post. I strongly believe that if you had the right teacher you could master computer vision and deep learning. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated?

Or has to involve complex mathematics and equations? Or requires a degree in computer science? All you need to master computer vision and deep learning is for someone to explain things to you in simple, intuitive terms. My mission is to change education and how complex Artificial Intelligence topics are taught. If you’re serious about learning computer vision, your next stop should be PyImageSearch University, the most comprehensive computer vision, deep learning, and OpenCV course online today.

Join me in computer vision mastery. Click here to join PyImageSearch University. Using this method we can obtain highly accurate face recognition, but unfortunately could not obtain higher than FPS. To be notified when future blog posts are published here on PyImageSearch, just enter your email address in the form below! Enter your email address below to get a.

Inside you’ll find my hand-picked tutorials, books, courses, and libraries to help you master CV and DL! All too often I see developers, students, and researchers wasting their time, studying the wrong things, and generally struggling to get started with Computer Vision, Deep Learning, and OpenCV.

I created this website to show you what I believe is the best possible way to get your start. Before you said it while going through the post I was also thinking what would it be like to run this on Intel Movidius NCS, would love to see a post on it in the future. Yes, I am serious. Googling for vocal fry can lead you to a lot of really, really bad cases of what it is.

You can contact me via the PyImageSearch contact form. Hello Adrian Rosebrock, I want to congratulate you for all your contribution in this field, I have a question and that is that I have mounted the topic of facial recognition, but the same program that I run on my laptop recognizes a distance of up to 5 meters but in the Raspberry device does not do it at 1 max and sometimes at 2 meters away, is there any way to overcome this problem?

That sounds like a difference in your camera sensors — your Raspberry Pi camera is not good enough to detect the faces from your distance. You can either:. Use a better camera sensor 2. Upsample the image prior to applying face detection — the problem here will be speed. The more data, the longer it will take to process. Thanks for this tutorial Adrian. This model dlib cannot be directly used by the Movidius NCS so a comparison cannot really be done. Regarding this topic, have you considered converting some Tensorflow model for face recognition, such as those provided with facenet by David Sandberg, to Movidius graph in order to increase FPS for face recognition on RPi platform?

Hi Adrian! I recently discovered your site and I love your tutorials. The caffe model from the previous post achieves between 1 and 0. Do you have any suggestions for using these two models together on a RPI? Thanks, Gus. Thank you for the wonderful post! Always wait for your post to learn new things. Thanks for creating this level of informative posts which anyone can learn, This post is also very informative and useful too.. What are you saying? Indeed, the Pi does have a GPU. Notice how the inference on a single image took 3.

Dear Dr Adrian, Thank you for this tutorial. My particular question is about increasing the frame rate. You informed us about using eigenfaces and local binary patterns LPB as a method of increasing the processing rate. Both do. I actually implemented the VideoStream class in this blog post to combine the two blog posts you are referring to into an easy to use class.

The code used in this post is already taking advantage of threaded video stream. Additionally, you should read this post on how to access the Raspberry Pi camera. Thanks for reaching out. Hello Adrian, First of all thanks for this tutorial. Congrats on configuring your Pi for face recognition, Daniel! It hangs after this. Hello Adrian, i really appreciate your work!

But i have a problem right now. I dont know why it is not working. Any idea? Check your CPU usage and let the Pi sit overnight just to make sure. You would need a model trained to recognize an object. Hi Adrian. Very informative and interesting, but also pedagogical.

You could have a centralized system for processing all frames but keep in mind network overhead — while the central machine is technically faster you also need to account for the time it takes for the frame to be transmitted and the results returned.

You might want to run some experiments to determine if that is viable. Hi Adrian, Thank you for the quick answer. Yeah, need to do some testing with network latency. I am facing issues in importing a once class SVM that I trained on my personal computer. Also, will the 1st generation Raspberry Pie work for this case, if performance is not a concern at this moment? Hey Patrik — could you be a bit more specific in what you mean by saying omitting a photograph?



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