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How Facial Recognition Works

Everything about How Facial Recognition Works

     

How Facial Recognition works

Facial recognition uses artificial intelligence (AI) to compare facial images and determine whether there is a match. It can determine whether two facial images are of the same person, and it can also determine whether a facial image matches any of the facial images contained in a particular database. These comparisons are referred to as 1:1 and 1:N matching respectively.

A brief background
While face recognition may seem like a new technology, it’s actually been in development since the 1960s. It is only in recent years, however, that it started to emerge as a viable technology that we can use at scale. The speed of the recent progress is due to various factors, including more powerful hardware (such as GPUs) and the advancements that have been made in machine learning and deep learning. Keep reading below to find out more!

Steps in the facial recognition process

While there are different AI algorithms [link to Face Matching Algorithms article] that can be used for facial recognition, they all follow more or less the same steps:

  1. Detect a face or faces in an image or video.
  2. Extract the facial features to create a faceprint or face embedding.
  3. Compare faceprints or face embeddings to determine whether there is a match. How similar the embeddings need to be for a match to be established will depend on the chosen confidence threshold. Comparisons can either be 1:1 (one-to-one) or 1:N (one-to-many).
  4. Return the result of the comparison or search.

 

Why is facial recognition important?

Facial recognition is transforming many aspects of daily life. Already, many of us are used to unlocking our smartphones or computers using Face ID. This has paved the way for facial recognition to be adopted for additional purposes, such as authenticating payments and enhancing access control.

Along with other forms of biometric identification and verification, facial recognition is transforming the landscape when it comes to applications ranging from access control and payment verification to retail and security. For this reason, it is important to understand how it works and how it could transform your own industry.

Click here to learn more about the different ways in which facial recognition technology can be used.

What are some of the common problems with face recognition, and how can we overcome them?

Face recognition isn’t perfect. While there has been huge progress, there is still room for error. Take a look at this example:

How facial recognition works

On a more serious note, here are some of the challenges with facial recognition:

  • Variations in images compared: When there are variations in the position of the head, lighting conditions, and facial expressions in the two images that you want to compare, it can result in lower accuracy. The same is true when it comes to make-up, glasses, hats, and anything that partially obscures the face. Luckily, there are a few ways to deal with these challenges. The first is to use a large and diverse training set. Second, deep learning techniques are making it easier to correct for these variations. 
  • Less than 100% accuracy: No face recognition algorithm is 100% accurate. Even with the most sophisticated software, you still have a chance that an authorized person will be denied access in an access control scenario or vice versa. In these types of situations, human intervention will still be required. However, with accuracy rates improving and with the technology achieving 98% accuracy and higher, it is clear that the technology is useful in all but the highest-risk scenarios.
  • Privacy: One of the biggest concerns surrounding the use of facial recognition technology is privacy. This is a valid concern, and therefore there are various measures that you can take to reduce the privacy risks when implementing facial recognition technology. These include: storing only faceprints/face embeddings, and not the facial images themselves; not storing facial image data at all (if your use case allows for this); storing and processing data locally, so that you have full control over the data and so that third parties do not have access; and purging data regularly.
  • Spoofing: In many instances, there are concerns that facial recognition software can be fooled by masks or copies of photos presented to the camera. To prevent this, many facial recognition programs incorporate liveness detection features, which ensure that the submitted video is of a live person, and that it is captured in real time.

 

How Sightcorp’s facial recognition technology works

Sightcorp’s facial recognition technology is 100% deep learning-based [link to Face Recognition Using Deep Learning article]. The technology uses Convolutional Neural Networks (CNNs) to detect faces, extract facial features, generate a faceprint, and compare the generated faceprint to one or more other faceprints to determine whether or not there is a match.

FaceMatch performs with state-of-the-art accuracy. It is also customizable, allowing you to determine your own confidence thresholds and to retrain the models on your own or your customers’ data.

With FaceMatch, you can:

  • Compare two facial images to determine whether they match (face comparison)
  • Search for a matching facial image within a database (face search)
  • Cluster similar faces together (face grouping)

 

Learn more about Sightcorp’s FaceMatch solution

Here are other articles you might find interesting:

 

   

Technical Specifications

The table below shows how FaceMatch SDK performs on the Labelled Faces in the Wild (LFW) dataset:

FPRTPRThreshold (Inverse of distance)
0.10.99900 ±0.002130.55448
0.010.99667 ±0.005370.59791
0.0010.99367 ±0.006050.62989

FPR = False Positive Rate
TPR = True Positive Rate

These results are an indication only and are based on the specific dataset Labelled Faces in the Wild. Customers can expect similar performance, with possible variations due to hardware and the availability of annotated data.