Do you want to look good in social media pictures?Filters are now common across social media, though they take different forms.If you are interested to know how face is identified, how different face landmarks like eyes, nose, lips, ears etc are identified and how filters are applied, book this free session.
Ever wondered how facial filters on social media platforms like Instagram really work? What tech did Instagram use to create real-time face filters?
You’ve probably seen face filters invading Instagram over the past few years. Just open the camera, point it towards your face, and the app will create a random filter for you.
Instagram face filters are the most common across social media, although they take different forms. Instagram offers beautification filters along with its augmented-reality facial filters, like those that add a cat’s ears and tongue to a person’s face. Snapchat also offers a gallery of filters where users can swipe through beauty effects on their selfie camera. Beauty filters in Tik-Tok’ and snapchat’s app are part of an app setting called “Enhance,” where users can set a standard beautification on any subject.
Book this free session if you are interested to know how the face is identified in real time, how different face landmarks like eyes, nose, lips, ears, etc are identified, and how filters are applied
In this session, we are going to talk about how to make instagram filter using computer vision and Machine learning.
So, let’s get started!!
An Instagram filter is a feature that allows you to edit your photo with one click, by simply applying preset edits to the image that Instagram has created for you.
Augmented reality (AR) filters are computer-generated effects designed to be superimposed on real-life images. AR filters work with your camera, adding a layer or imagery in the foreground or background of your image. There might be a good chance you’ve come into contact with an AR filter in one way or another on Instagram.
Instagram facial filters are the hidden secret to online growth, increase in brand recognition and gaining attraction for you or your project – and with your own interactive and engaging AR effects your profile will stand out from the crowd.
Creating the face filters for Instagram is not that much of a challenge. Now you can also create your own Instagram filters using Machine Learning and computer vision. This session will walk you through every step to create your very own interactive, animated 3D face filter, that when finished, you can upload and share with your friends and family!
Computer vision technology and applications are exploding right now! With several apps and industries making amazing use of the technology, from billion-dollar apps such as Pokémon GO, Snapchat and up and coming apps like MSQRD and PRISMA.
Even Tesla, Microsoft, Google, Facebook, Apple, and Amazon are all utilizing computer vision heavily for self-driving cars, face detection, object recognition and image searching!
Face Detection also known as Facial Detection is an AI-based computer technology that helps to identify human faces in digital images. Face Detection is used to detect faces in the images and the very first step for face recognition. Face detection can be applied to various domains like security, law enforcement, biometrics and personal safety -- to provide tracking of people in real time.
Face detection algorithm is used in cameras to identify multiple appearances in the frame Ex- Mobile cameras and DSLRs. Instagram is also using face detection algorithm to create their facial filters.
Face mask detection is an advanced version of opencv face detection. Face mask detection uses visible streams from the camera combined with AI techniques to detect and generate an alert for people not wearing face masks.
Face mask detection algorithms work by searching for human eyes - one of the easiest features to detect. The algorithm then attempts to detect eyebrows, nose, nostrils, the mouth and the iris. Once the face detection python algorithm concludes that it has identified a facial region, it applies additional tests to confirm that it has, in fact, detected a face with a mask.
In the real world, all the data we collect are in large amounts. To understand this data, we need a process. Manually, it is not possible to process them. This is where the concept of feature extraction in machine learning comes into picture.
Feature extraction in image processing is a part of the dimensionality reduction process, in which an initial set of the raw data is divided and reduced to more manageable groups. So when you want to process that data, it will be easier. The most promising characteristic of these large data sets is that they have a large number of variables. These variables require a lot of computing resources to process them. So Feature extraction helps to get the best feature from those big data sets by selecting and combining variables into features, thus, effectively reducing the amount of data.
Facial landmarks are used to align facial images to a mean face shape, so that after alignment the location of facial landmarks in all images is approximately the same. However it makes sense that facial recognition algorithms trained with aligned images would perform much better, and this intuition has been confirmed by many research papers.
Machine learning (ML) is a category of an algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data, use statistical analysis to predict an output.
Machine Learning has been a buzzword for the past few years, the reason for this might be the high amount of data production by applications and the increase of computation power in the past few years.
Machine learning can be classified into 3 types of algorithms.
Let see how machine learning is changing your day-to-day life
Enroll now for the upcoming session and learn how to make your own filter on Instagram.