Computer Vision: Facial Recognition for Businesses

The first question smart executives ask when reading about a new business tool is, “How would this impact our bottom line?” Investments in technology need to be justified by reducing operating costs, boosting productivity, or increasing revenue. A good example of technology which is starting to earn its way is facial recognition. It’s being used to power more accurate medical diagnoses, process image-related data, and offer advanced security features to social media users. From the amount of interest, a lot of big players have seen potential enterprise value in facial recognition. The technology has seen an annual growth rate of 27.7% from 2013 to 2018. Experts predict the market will reach $6.5 billion by the end of next year. This growth is driven by the wide variety of potential applications. Read on for a look at how facial recognition is making waves in healthcare, social media, and even BI.

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Better medical diagnoses

There are subtle facial differences that can indicate certain underlying genetic conditions. Some, like Down’s Syndrome, present as a widely recognizable collection of features. Others, like Sotos Syndrome or DiGeorge Syndrome, are signaled by dozens of tiny details too obscure for a non-specialist to catch. Facial recognition software scans a patient’s face and measures a host of characteristics like eye positions, nasal bridge angle, and relative proportions. Two or three traits aren’t significant; dozens of features must be analyzed in relation to each other, then weighed against other symptoms to present a possible diagnosis. Doctors confirm these diagnoses with additional tests, but so far the software has high levels of accuracy. What makes this application of facial recognition so exciting is how early a diagnosis can be made. Without this technology, patients aren’t diagnosed until the most debilitating trademark symptoms of their conditions emerge. With it, doctors are able to plan ahead to provide the best possible care and quality of life.

Smarter privacy filters

Snapchat patented a technology that uses facial recognition to spot pictures of people in other people’s images- even unrelated people. If the pictured person has toggled a specific privacy setting, the software would cover their faces with emoji. Users could also preemptively deny sharing their picture without asking permission. Right now Snapchat relies on object recognition, not facial recognition. The difference is in specificity: object recognition identifies that something is a face while facial recognition identifies the person pictured. This software hasn’t been released yet, but it would be a leap forward in social media privacy features. Consumers show a lot of interest in being able to control what others share about them. The first social media platform to meet that need will see a surge in subscriptions and usage.

Fool-proof biometric authentication

Passwords can be hacked, but bodies are harder to duplicate. Engineers have been working to improve biometric authentication methods since the first fingerprint scanners were released. Facial scans are regarded as the Golden Fleece of biometric security because of the massive number of data points involved. They’re much safer than fingerprints. There’s a one in 50,000 chance that a random person could be a close enough match to pass a fingerprint scanner; with facial recognition, those odds drop to one in a million. Old models were laughably easy to fool, sometimes even unlocking when presented with photographs of the authorized user. Sophisticated artificial intelligence algorithms makes that unlikely now. Even Hollywood special effects artists can’t fool the latest crop of facial recognition tools. Mobile devices are leading the push to bring facial recognition to consumers. Apple’s iPhone X and FaceLock for Android plan to offer the tool as a log-in option.

Automated image clustering

Analyzing unstructured data is tough enough with text. Faces are orders of magnitude harder for computers to process. With facial recognition technology, computers apply image clustering to identify and sort pictures of people as easily as text. This is used to make sense of huge collections of consumer data. Image clustering helps in spotting trends, like what celebrities are generating interest and which are often viewed together. It’s also useful in gathering data about customers to help in segmentation and intelligent profiling.

Hyperefficient Tagging

The average person uses 5 social media platforms. Monitoring them all requires a significant investment of time, so companies are always looking for ways to make their platform more user-friendly. Artificial intelligence has improved enough to identify individual faces regardless of strange positions or lighting. Facial recognition can be more reliably used to power automatic tagging, increasing platform usage and engagement by reducing the time it takes to manage the account. It also helps people spot and report undesirable images of them being shared without their knowledge.

Looking Forward

In a very real sense, faces are data. Facial recognition technology is the tool needed to make sense of that data and use it to make better informed decisions. Interested in making better data-driven decisions? Concepta offers free consultations on how our artificial intelligence solutions can guide your corporate strategy.

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