Machine Learning has impacted all the fields of operation in today’s world. So, the chances are that you have already felt the impact of machine learning in your daily life, whether or not you are professionally involved with it. It is also highly possible that you are already using various tools and products that rely on machine learning. These include smart assistants like Alexa or Siri, smart TVs, and automated cars, to name a few.
Even seemingly simple, day-to-day applications, like Netflix, use data and machine learning to predict what titles to display in which locations, depending on user behaviour and other things. Likewise, other streaming platforms like Hotstar, Prime, Spotify, Apple Music also rely on Machine Learning in one or the other. Even social media platforms employ ML algorithms to make the experience more personalised for users and deliver content they want. This is also true for shopping platforms like Amazon, Flipkart, etc.
The list goes on and on for machine learning and its application. In that list, one of the more important use cases – both from a consumer products point of view and research point of view – is that of facial recognition or face recognition using Machine Learning. This blog will look at what face recognition is and how it works with machine learning.
What is Face Recognition?
Face recognition refers to the process of giving machines, tools, and software the ability to identify or verify different facial features. Its primary use case is for security and biometric settings, though it is also equally used in various areas.
Face recognition is one of the technologies that has received much attention from academicians and innovators alike. As of today, there are numerous different face recognition techniques in practice. Most of these systems work based on the various nodal points on a human face. The values derived from the variables associated with these points help identify a person. This technique allows applications to quickly and accurately identify individuals and is extremely useful for security contexts. These techniques are constantly evolving with novel approaches such as 3-D modelling, which helps overcome the drawbacks of current processes.
The face recognition technique presents numerous benefits, especially compared to other biometric techniques. First of all, this is an entirely non-invasive nature as it requires no contact with the person being verified. Just simple scanning will do the job. Face images can be easily captured even from a distance and analysed as needed.
Because of these benefits and more, there is continuous ongoing research to make face recognition techniques more effective and sophisticated. For the most part, Machine Learning has been able to simplify a lot of things and provide efficient face recognition algorithms and systems. It is still a growing field, but the start of face recognition with machine learning has been a fruitful one.
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Let’s look at the role of Machine Learning in making face recognition more efficient and sophisticated.
Face Recognition with Machine Learning
Face recognition techniques have constantly matured and evolved alongside the advancement in machine learning, deep learning, artificial intelligence, and other related technologies. For example, machine learning algorithms quickly find, capture, collect, analyse, and retrieve different facial features and nuances to match them with pre-existing images to form a connection. Machine learning in face recognition has already proved its mettle in various fields, including security and biometrics, but not limited to it.
Exactly how facial recognition works using machine learning is something slightly technical and goes beyond the scope of this introductory article on face recognition using machine learning. So for this article, let us consider the five broad problems that need to be solved by machines to successfully and correctly recognise a face. Here are those five problems:
1. Face Detection
The process of correctly recognising faces begins with first detecting faces from a set of objects. By now, many smartphone cameras come with an inbuilt face detection module. It is also available with social media platforms such as Facebook, Instagram, Snapchat, etc., using which users can add different effects and filters to their photos.
2. Face Alignment
Faces that don’t look directly at the camera or those away from the focal point are interpreted as completely different by the computer. That is why, a machine learning algorithm is needed to normalise the face in question to make it appear consistent with the faces stored in the database. This is generally done by using generic facial landmarks. These could include the outside of the eyes, top of the nose, bottom of the chin, etc. Then, the ML algorithm is trained repeatedly using different data points to locate these points on the face and turn them towards the centre to align to match the database.
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4. Feature Extraction
This is another crucial step that helps extract all the essential features and characteristics from the face that will then help in the final matching of the face to other faces in the database. For a long time, it was unclear which feature should be extracted and looked for. Eventually, researchers concluded that it is best to let machines and algorithms identify the features it needs to collect for best matching. In technical terms, this process can be called embedding, and it uses deep convolutional neural networks to train itself. Then, it generates multiple measurements of the face, making it easier to distinguish the face from other faces.
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5. Face Recognition
Once the unique features and measurements of the face are extracted in the feature extraction stage, another ML algorithm is required to match these measurements against other faces stored in the database. Whichever face from the database comes closest to the features will be a match for the input face.
6. Face Verification
Face verification is the last step in the entire face recognition process using the machine learning process. In this, the ML algorithm is required to return a confidence value to confirm whether the face matches or not. Depending on that, the next iterations are performed to improve the matching or declare the result.
In Conclusion
Machines are getting smarter, and there’s no denying that. At this point, it is your choice to make whether you want to sit back and watch the machines get smarter or do you want to actively be a part of this change. The best part about this field is that it is open to and invites people from all different backgrounds, ranging from computer science to psychology, economics to electrical engineering, and more.
At upGrad, we can confidently say that if you are motivated enough, you can build a fruitful career in Machine Learning. With dedicated guidance and an industry-standard curriculum, our Master of Science in Machine Learning & AI will introduce you to the complete range of ML – from basics to advanced levels. The program includes numerous real-world assignments and a capstone project to let you get handsy with ML algorithms and techniques.
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