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Figure 5: Here are Trisha and I, ready to start our vacation! Figure 6: Recognize faces in video streams As a bonus, I decided to include a section dedicated to OpenCV face recognition in video streams! Our three models and label encoder are loaded here: From here everything is the same as Step 3. Cleanup by closing windows and releasing pointers Lines and To execute our OpenCV face recognition pipeline on a video stream, open up a terminal and execute the following command: Face recognition in video with OpenCV.
As you can see, both Trisha and my face are correctly identified! Drawbacks, limitations, and how to obtain higher face recognition accuracy Figure 8: All face recognition systems are error-prone. What do you do in those situations? And how do you improve your OpenCV face recognition accuracy?
In my previous tutorial on face recognition , a handful of PyImageSearch readers asked why their face recognition accuracy was low and faces were being misclassified — the conversation went something like this paraphrased: What can I do to increase face recognition accuracy? How many face images do you have per person? Only one or two. Gather more data. I get the impression that most readers already know they need more face images when they only have one or two example faces per person, but I suspect they are hoping for me to pull a computer vision technique out of my bag of tips and tricks to solve the problem.
In general, I would recommend a minimum of faces per person. The OpenCV face recognition system we discussed here today worked but can always be improved. Perform face alignment Figure 9: Performing face alignment for OpenCV facial recognition can dramatically improve face recognition performance.
The face recognition model OpenCV uses to compute the d face embeddings comes from the OpenFace project. The OpenFace model will perform better on faces that have been aligned. Face alignment is the process of: Identifying the geometric structure of faces in images.
Attempting to obtain a canonical alignment of the face based on translation, rotation, and scale. Detected a faces in the image and extracted the ROIs based on the bounding box coordinates.
Computed the centroid for each respective eye along with the midpoint between the eyes. And based on these points, applied an affine transform to resize the face to a fixed size and dimension. If we apply face alignment to every face in our dataset, then in the output coordinate space, all faces should: Be centered in the image. Be rotated such the eyes lie on a horizontal line i.
Be scaled such that the size of the faces is approximately identical. Tune your hyperparameters My second suggestion is for you to attempt to tune your hyperparameters on whatever machine learning model you are using i. Still, if your face recognition accuracy is not sufficient, it may be worth the extra effort and computational cost of tuning your hyperparameters via either a grid search or random search.
The k-NN model worked extremely well, but as we know, more powerful machine learning models exist. Each week I receive emails that paraphrased go something like this: My error looks like this: There are three separate Python scripts in this tutorial, and furthermore, each of them requires that you correctly supply the respective command line arguments.
Make sure you walk before you run, otherwise you will trip up. Take the time now to educate yourself on how command line arguments. Secondly, I always include the exact command you can copy and paste into your terminal or command line and run the script.
Program in the IDE, but use the command line to execute your scripts. Mean Squared Error vs. Equation 1: On Line 7 we define our mse function, which takes two arguments: All the real work is handled on Line We then take the difference between the images by subtracting the pixel intensities.
Line 12 handles the mean of the Mean Squared Error. All we are doing is dividing our sum of squares by the total number of pixels in the image. Finally, we return our MSE to the caller one Line MSE is dead simple to implement — but when using it for similarity, we can run into problems.
A value greater than one implies less similarity and will continue to grow as the average difference between pixel intensities increases as well. In order to remedy some of the issues associated with MSE for image comparison, we have the Structural Similarity Index, developed by Wang et al.
Equation 2: There is a subtle difference between the two, but the results are dramatic. Doing this leads to a more robust approach that is able to account for changes in the structure of the image, rather than just the perceived change. Compare Two Images Using Python import the necessary packages from skimage. The mse function takes three arguments: It actually turned out pretty good! We are sorry for your inconvenience.
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Review by Frederick Barton on 01 May, If an image is large enough, it can be easier to edit or print it, but if you need to upload or send it, its large size can be a drawback. Some applications can be slowed down when using large images, so the ideal solution is to resize them.
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