How Does Facial Recognition Work?

how does facial recognition work -magoosh

A facial recognition system is a computer application that can identify a person from his/her image. One way to recognize faces by computers is by comparing selected features of the face in the image and a database of facial images.

Algorithms for Facial Recognition

Since a long time, scientists and researchers have been trying to develop powerful face recognition algorithms that can be used to accurately and efficiently recognize faces of humans. Such systems will have huge commercial value. To give some examples:

  • Facial biometrics – using faces could serve as another authentication mechanism. Apple has already launched this in the form of Apple’s FaceID.
  • Targeting – various American companies are using real-time face recognition to identify the people entering their stores to target the most valuable customers. These customers are then given a better and a highly personalized customer service to make sure that they stay as valuable customers.

Let us understand some algorithms that have been used in the past to solve the problem of face recognition.

Traditional algorithms involving face recognition work by identifying facial features by extracting features, or landmarks, from the image of the face. For example, to extract facial features, an algorithm may analyse the shape and size of the eyes, the size of nose, and its relative position with the eyes. It may also analyze the cheekbones and jaw. These extracted features would then be used for searching other images that have matching features.

Traditional algorithms have proved to be highly inaccurate as well as inefficient. These algorithms have not given good results and they are not scalable because there are many people who have similar facial features.

Over the years, the industry has moved towards Deep Learning. Convolutional Neural Networks have been employed lately to improve the accuracy of face recognition algorithms. These algorithms take image as input and extract a highly complex set of features out of the image. These include features like width of face, height of face, width of nose, lips, eyes, ratio of widths, skin color tone, texture, etc. Basically, a Convolutional Neural Network extracts out a large number of features from an image. These features are then matched with the ones stored in the database.

Convolutional Neural Networks have proved to be far better than traditional algorithms. However, the biggest challenge that remains is that of scaling. These algorithms require heavy resources and computation to produce tangible results. Therefore, scalability is still a big issue.

Final Words

Face Recognition however is being widely researched by the researchers. There has been a steady increase in the computation power (Moore’s law) due to which the accuracy of face recognition algorithms has gone up significantly. With continued research and development, this growth is likely to continue in the future too!

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