The Approach To Facial Recognition With AI-Based Machine Learning

Today’s technology has made facial recognition more prevalent than ever. This is primarily due to AI-based machine learning. Machine learning technology now gives computers the capability to learn without explicitly being told to do so by a user. These computers can learn from data, which allows them to make predictions based on data that they have not yet received. Machine-learning computers don’t exhibit a relationship between its inputs and its outputs.

Facial recognition, as it relates to deep learning, is incredibly complex. Below, we’ve identified a few of the challenges presented by facial recognition technology and AI-based machine learning. We’ve also introduced the three primary approaches taken to overcome these challenges.

Deep Learning And Facial Recognition Challenges

Although deep learning is one facet of AI-based machine learning, although it appears to be the most prominent. Deep learning is based on the artificial neural network. This network is one type of machine-learning technique, inspired by the function and structure of the human brain. These machine-learning devices take extensive and complex data sets and process them inputs, allowing them to better-predict outcomes in the future.

The problem presented by deep learning is the amount of data necessary to yield reliable outputs. Consider this as it relates to facial technology. When analyzing a 100 x 100 facial image, the possible combination of intensity values is roughly 280,000. This is because of interpersonal variations that occur at even the smallest scale.

Have you ever looked at someone from far away and thought they looked familiar, only to realize that upon closer inspection they did not look as you expected? This is one of the challenges with facial recognition technology. Images of two different people may appear similar because of poor photo quality. The machine requires large, more precise datasets to more reliably predict the outcome. What approaches are there take to ensure a reliable output?

Modern Approach

This approach believes that with enough inputs, the neural network will find features on its own. Some of the largest corporations in the world use this technology, including Facebook and Google. This approach is beneficial because large datasets and inputs prevent false readings that typically occur due to:

• Poor lighting or illuminations

• Varying poses

• Other similar factors

This approach uses a chain process to reach its outputs. During the first phase, the machine uses pre-processed images to identify a face. Then, the device will verify that there is, indeed, a face in the picture. If there is a face in the image, the computer will identify landmarks to enhance facial alignment further. At this point, deep learning tools can accurately detect the individual in the picture.

Classical Approach

This approach, which was first used at the onset of facial recognition technology, chooses features based on domain knowledge of the data. These features are then fed to a machine-learning algorithm. This process is still in use today and works best for those with access to smaller data sets.

Unfortunately, this approach poses problems when it comes to identifying someone is a low-light situation or different poses. However, if the individual in question is in the same, or similar-enough pose, such as in an identification picture, this approach should be useful.

Histogram Of Oriented Gradients Approach

Deep learning machines use the histogram of oriented gradients to process images and detect objects. The device does so by taking a localized portion of the image and then counting the number of gradient occurrences.

Once this is complete, the machine can then extract specific features related to shapes and edges. So, for instance, the devices will first remove your face from a large image and align it for comparison with a reference image. This is the most advanced facial recognition technology available.e

Are You Looking To Implement One Of These Approaches Into Your Security Techniques?

Those who use verification to prevent fraud, ensure compliance, or secure their business will likely be interested in the approaches listed above. Forrester states that facial recognition makes authentication more secure and that it can:

• Reduce help desk calls due to lost passwords

• Expedites mobile logins

• Prevents fraudulent logins

Recently, we’ve seen this technology take place at the consumer level, as many smartphone users now use their facial recognition software to access their devices and complete tasks, such as obtaining their banking information or authorizing a purchase. Forrester said that not only should security and risk departments consider the technology, but so too can marketing professionals. As deep learning systems become more accurate so too will their facial recognition outputs.

Fortunately, as facial recognition technology has become more mainstream so too has its accessibility as a large-scale security tool. If you’re looking for the latest Automated ID Authentication and secure customer onboarding solutions, be sure to check out what we offer our clients. We specialize in providing Selfie-to-ID face matching with weighted multi-modal spoofing detection, as well as a host of other security solutions.

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