In today’s world, most data collected is made up of a lot of images as well as videos. This is one of the significant reasons why image processing for translating as well as obtaining information is quite essential for businesses.
Face Recognition is known as the latest trend when it comes to various machine learning techniques. Today, we will have a look at the best Python libraries and frameworks for face detection.
Remember, all of these are truly amazing and have been loved by people out there.
List Of Python Libraries And Frameworks for Face Detection:
At number one on our list, we have got Scikit-image. This platform basically uses NumPy arrays as image objects. It does this by transforming the original pictures.
Moving on, the ndarrys can be integers or floats. Integers are either signed or unsigned. Moving on, the NumPy is built in C programming. This has excellent speed and is fantastic for image processing.
Moreover, data scientists also use the greyscale technique apart from various other methods as well. In this technique, each and every pixel is basically a shade of grey.
Scikit-image is free of charge as well as free of restoration. Sounds good, doesn’t it? It works exceptionally well, and people simply love it.
OpenCV was released in the year 2000. It has gained massive popularity since it is pretty easy to use. Moreover, its readability is top-notch as well, which has been one of the drivers of increasing its popularity.
This library basically focuses on lots of features. Some of these include image processing, object detection, as well as face detection.
It is actually written in C++ and also comes with a Python wrapper. It is capable of working with Numpy, SciPy, as well as Matplotlib.
Moreover, the library has been backed by loads and loads of contributors on GitHub.
Third, on our list, we have got Mahotas. This is a highly amazing platform that offers some advanced features for users. Some of these include local binary patterns, haralick, and many more.
Moving on, the feature that makes it pretty unique is that it has the ability to commute 2D as well as 3D images. It does this through the mahotas.features.haralick module.
After that, it starts an advanced level jacket processing by making sure to extract every information from the pictures.
The platform has more than 100 functionalities when it comes to computer vision capabilities that keep growing every now and then.
Some examples of its functionality include watershed, thresholding, Sobel edge detection, convolution, convex point’scalculations, morphological thinning, and more.
Moreover, Mahotas includes different algorithms that are implemented in C++, which gives it incredible speed when working in NumPy arrays. Not to add, it also has a clear and clean Python interface.
The platform also comes up with new features every now and then and improves its performance as well, so you’ve got nothing to worry about.
Next up, we have got Simplel TK. This library is different from the ones that think of images as arrays. Simplel TK actually thinks of images as a set of points on a physical region in space. Sounds interesting, doesn’t it?
In simple words, images here are actually thought of as spatial objects, moving on; the computations here are performed in 2D, 3D as well as 4D.
Moreover, the region that is occupied by images is defined as the size, origin, direction cosine matrix, and the spacing.
It also consists of loads of filters for image segmentation workflows. These include Otis thresholding, level sets, as well as watersheds.
There are many tools as well that can be used to evaluate segmentation results. Some of these are Jaccard, Dice Values, Surface Distances, and more.
Not to add, these tools can also analyze the segmented shape characteristics. These include oriented bounding box, perimeter, elongation, Feret Diameter, principal moments, and many more.
On number 5, we have got SciPy. This library is basically used for mathematics as well as scientific and engineering computations. However, one can also implement algorithms for image manipulation.
For image manipulation, you need to import the scippy.ndimage module. Moreover, you also get to carry out binary morphology, object measurements, linear and non-linear filtering as well.
Moving on, you can also draw stuff like contour lines, add effects, filter, adjust the interpolation, denoising, and other segmentation on images.
Moreover, the interface is quite amazing as well as fast. This means users can face no issue when working with this library.
Here we have got an advanced version of PIL called Pillow. It is supported by Tidelift and consists of some incredible processes when it comes to image processing.
Some of its processes include point operations, manipulating, filtering, along with many others. Moving in, it also supports a range of different image formats.
The library actually comes with loads of powerful image processing capabilities, and that is what makes it highly amazing.
It provides a solid foundation when it comes to a general image processing tool. Moreover, it has been licensed under the HPND, just like PIL. This way, you won’t have to worry about it being legit or not.
Next up, we have got Matplotlib. This library is usually used for 2D visuals. However, one can also use it for image processing.
The platform doesn’t really support all file formats; however, it works exceptionally well when it comes to altering images to extract information.
The platform is known as a comprehensive library in order to create static, animated, as well as interactive visualizations in Python.
There are lots of incredible features that are offered by the platform as well that makes your experience quite amazing.
You can also have a look at many tutorials offered on the library’s website in order to get a better understanding of what they provide to their users.
Here we have got the Eigenface Recognizer. Now, this algorithm works in a different manner and believes that not every part of the face is essential and useful.
For instance, when we look at someone, we check out their distinct features. These include their eyes, nose, cheeks, and forehead. All of these vary in terms of each other.
The algorithm basically focuses on areas of maximum change of the face. For instance, when we look from the eyes to the nose, we can see a vast difference. The same is the case when we look from the nose to the mouth.
Now, the thing is, when we look at various different faces, we end up comparing them by looking at distinct features of the face. These are the ones that are pretty important as well as useful.
They hold great importance as these as they are the ones that make our faces different from another. In other words, this change is what basically helps us in differentiating one face from another.
Eigenface Recognizer actually looks at all the images of people and ends up extracting the components that are the most useful and important.
Moreover, it ends up discarding the rest of the components; they don’t really play a role. These components extracted are called the principal components.
It also comes with a feature that keeps a record of the principalcomponents extracted in order to figure out which element of principal belongs to which person.
On number 9 of our list, we have got the FisherFaces Face Recognizer. This is an algorithm that is an advanced and an improved version of EigenFaces.
The thing about this platform is that it looks at all the training faces of people and finds their principal complements. The definition of the principal components has been mentioned earlier.
Moving on, once the principal components from all the training faces are combined, one is able to focus on the features that actually represent all the people in the training data.
However, when we talk about FisherFaces Recognizer, its approach comes with a number of different drawbacks. For instance, images with sharp changes end up dominating the rest of the images.
This drawback is something that can ruin the entire task. This way, one ends up with features that are from external sources and may not be useful at all.
However, FisherFaces is actually known as one of the most popular algorithms out there when it comes to face recognition. It is known to be relatively superior to other techniques as well.
The algorithm does an incredible job and has been used by loads of people out there.
Here we have got the Local Binary Patterns Histograms Face Recognizer that is used all over the world for various tasks.
Now, the thing about FisherFaces and EigenFaces was that they are affected by light. Moreover, when we talk about real life, the perfect kind of light conditions can not always be available for one.
This is where Local Binary Patterns Histograms plays a significant role. It is known as an improvement to overcome this issue that FisherFaces face.
This algorithm ends up finding the local structure of an image and then compares each and every pixel with its neighboring pixels. This technology is absolutely amazing!
Next up, we have got IPSDK. It is known as an image processing library in C++ and Python. This incredible library comes with some incredible features in order to process data set.
Moreover, the library is also capable of processing a comprehensive as well as an optimized range of functionalities when it comes to both, 2D and 3D processing.
The platform is quite fast and has the ability to complete the task in no time. Some of its features include high performance, full PC cluster support, as well as high availability computing. How great!
Wrapping It Up
Image processing techniques are becoming quite popular with each passing day in various industries worldwide.
They are used for many cases and have also become a massive part of data science and artificial intelligence.
Here were some of the best Python libraries and frameworks for face detection out there. All of them are pretty amazing and can do wonders.
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