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Face Recognition vs Face Detection: What’s the difference?

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Ever wonder what the difference is between Face Recognition and Face Detection? Well, you’re not alone. In fact, in our industry, we find that face recognition and face detection are two terms that are commonly misunderstood and as a result, tend to be used interchangeably. If you do a quick Google search, you’ll see exactly what we mean. In this article, we will shed light on these two technologies and give you an overview of what they do.

In recent years, face recognition has owned a significant amount of consideration and is appreciated as the most promising application in image and video analysis. Face detection is a significant part of the facial recognition processes. It is the first step towards facial recognition. A face detection system uses pattern detection algorithms to determine the presence, location, scale, and orientation of any face present in a still image or video frame. Because it uses pattern detection algorithms, it is anonymous by definition. No attempt is made to positively identify a person in an image or video. This means that it does not store any images and no personal information is collected. The only data that is stored is of an anonymous, aggregate, statistical nature.

What is Face Recognition?

Face recognition, as you might have gathered now, is a biometric technology that does more than just detect a human face in an image or video. It goes a step further to establish whose face it is. A face recognition system works by taking an image of a face and making a prediction about whether the face matches other faces in a provided database. The technology is designed to compare and predict potential matches of faces regardless of their expression, facial hair, and age. Confidence factor is a key metric to avoid improper identification. As you can imagine, this technology is a valuable tool for security industry specialists. Financial institutions have also adopted facial recognition to streamline KYC processes and provide a frictionless banking experience for the customers.

With the advancement in technology, there are also many other applications for this technology. For example, you can use your face to unlock your phone and operate specific mobile applications.

How businesses use Facial Recognition for effective marketing

Audience measurement is an integral tool for both data analytics solution providers and brand marketers. Although you’ll find several platforms for big data analytics, only a few of them feature the unique algorithms that analyze human reactions to digital signage content using video cameras to capture viewer responses. The advent of Artificial Intelligence along with other emerging technologies is making it possible to analyze the viewer’s gender, age, emotional reactions, and attention span.

Digital signage is becoming a common technology and getting more advanced each year. Face recognition is being combined with digital signage to target the message to those viewing the signage at a specific place and time. The technology is also being used to analyze the viewers and their interests. This is valuable for companies looking to maximize their advertising and reach the target market effectively. Combining gender and age recognition systems can help retailers target either men or women falling in a certain age bracket. This would give them the benefit of using data to understand peculiar behaviors of customers in the various age brackets and sexes, an insight that has become quantifiable thanks to advances in both digital marketing and technology.

Besides targeting campaigns, the analytics can come in handy to establish a retailer’s customer base that would help in refining floor layout, shelf displays, and inventory. Retailers can as well evaluate how effective content is by comparing data on store sales at a particular time content was played.

Advances in technology and the need to target advertising match in face recognition and detection systems. This has helped retailers identify their market and identify unique customer behaviors. This is an insight that would otherwise not be possible without face recognition technology.

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Melissa Roux Author
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About the Author
Hobbies: reading, hiking, football and traveling. Why I'm an expert: I have 6 years of experience in copywriting; I'm always learning new things; I started out my marketing career in the education sector, where I had the opportunity to learn about multiple fields; I started working in the tech industry last year and find it highly engaging