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Facial recognition is improving customer experience while enhancing security

Facial recognition is improving customer experience while enhancing security
Convenience and ease of use are fast becoming the primary way that financial institutions can attract as well as retain customers and fortunately, the upcoming security measures such as facial recognition are improving customer experience while providing effective security in other sectors as well.Convenience and ease of use are fast becoming the primary way that financial institutions can attract as well as retain customers and fortunately, the upcoming security measures such as facial recognition are improving customer experience while providing effective security in other sectors as well. In retail, facial recognition enhances the in-store customer experience and also improves retailer operations.
Convenience and ease of use are fast becoming the primary way that financial institutions can attract as well as retain customers and fortunately, the upcoming security measures such as facial recognition are improving customer experience while providing effective security in other sectors as well. In retail, facial recognition enhances the in-store customer experience and also improves retailer operations.
 
Facial recognition is becoming an ever-larger part of not just the surveillance industry but also of the biometrics market and digital transformation efforts across the globe. Various segments of the quite diverse facial recognition market are poised to grow faster than previously expected.
 
As investments in facial recognition technologies are increasing and technologies are maturing, we are seeing an increase in facial recognition use. The COVID pandemic has proven to be an essential driver here. But given its much-improved performance and convenience, it’s important to understand what exactly facial recognition is.
 
Facial recognition technology is based on its ability to quickly and accurately match what the device camera sees with an image that has been saved either on a server somewhere or on the device itself. When there is a strong match between the two, the device using recognition allows a user to gain access to an account or a physical place. In other words, the technology verifies that a legitimate user has access to a device, confidential or sensitive personal information, or something like a restricted room, building, or car.
 
For the technology industry, the growing facial recognition market overall is a financial blessing, pure and simple. The focus is often on artificial intelligence, machine learning (mainly deep learning), and machine vision technologies that enable the matching of images from cameras with images in databases and complex facial recognition systems, which are enhanced by far more technologies. There is the hardware, the infrastructure to capture and interpret the data with facial recognition analysis tied to edge computing, the connectivity aspect, the software, the services, the list goes on.
 
With the COVID-19 pandemic, facial recognition technologies also increasingly get used in digital healthcare and disease outbreak prevention by combining it with other types of biometrics and in applications, ranging from identifying people with protective headgear to applications including, for instance, temperature detection.
 
Many immediately think about things like airports and border checks, surveillance, and similar applications when hearing about facial recognition, but facial recognition applications are also tested in retail facilities (not just for security, but also for self-service checkout and far more).
 
Facial recognition technologies are also used by social networks, for digital marketing purposes, in healthcare (patient screening), in voting (gov), for access to specific facilities, for criminal investigations, and some say it’s vital for the future of mobile banking and mobile commerce (secure mobile payments and authentication).
 
Retail stores have now become more of experience centers than just sales outlets. To provide a connected and engaging in-store experience, retailers continuously integrate innovative technologies. A couple of examples are knowing campaign effectiveness through facial behavioral analytics with AI-enabled cameras on digital signage, sending personalized promotions on mobile devices by detecting customer location in the store, using interactive kiosks and digital displays to browse the entire product catalog. Moreover, facial recognition has brought shopper identification to a whole new level with advancements in AI technologies.
 
The concept of personalization when integrated with facial recognition technology has one of the most obvious means to improve customer experience, while, at the same time, enhancing security. By matching up faces in real-world environments to those on a database of a hotel or some other company can identify people quickly and tailor services for those people.
 
Hotels, for example, can offer guests the option to provide a photograph of themselves during the booking process. When the camera at the hotel identifies their face upon arrival, hotel staff can then go and greet them by name and use the booking information to ensure they get a service that is more specific to them. It can also be used to identify guests who have stayed at the hotel before, allowing them to be rewarded for their repeated visitation or to identify those customers who do not have a booking with the hotel.

The challenges that can be expected in deploying facial recognition

Facial recognition has been adopted across many industries. It is relatively easy to integrate and deploy, but it has also given people a sense of using a technology that is more advanced and secure than passwords or PINs, enhancing the user experience. However, on the path to deploying what many consider to be the optimal biometric solution, much is still misunderstood, creating some pretty significant blunders along the way.
 
In the early stages, the system was beset with problems that highlight the difficulty of using algorithms to enhance any existing closed-circuit television security system. First, without clear, unambiguous images to compare, the system failed. It was often inaccurate to the extent that it identified some people in the street multiple times as matches, even though they were not in the suspect database. Another issue was that the test used two algorithms that produced drastically different results—a problem that could jeopardize the use of the technology as evidence in court. Finally, the police underestimated the level of training required for personnel to become effective, so costs escalated.
 
What requires our attention in this regard are the elements that affect the performance of facial recognition technology. The performance of many state-of-the-art facial recognition methods deteriorates with changes in lighting, pose, and other factors. Those factors which can affect system performance are summarized into four types: technology; environment; user; and user-system interaction.
 
It is quite easy for the camera to capture high-resolution and stable face images from a near or close distance, but when it comes to facial recognition at a distance (FRAD) systems, the quality of face images becomes an issue. The user-system interaction in middle to far facial recognition systems is not as simple. To build a robust FRAD system, these issues should be solved: resolution, focus, internal effect, and motion blur.
 
In fact, the deployment of high-density networks of AI-supported security cameras to monitor anything is most probably the first significant area where 5G cellular Internet of Things (where 5G and IoT meet) can have a considerable impact. Or, more specifically: the usage of high-density networks of AI-supported security cameras. This isn’t just the case in homeland security but also in securing critical facilities or even smart cities and other communities.
 
There are other issues that a facial recognition system faces. Illumination, which stands for light variations, becomes a problem as the slight change in lighting conditions causes a significant challenge for automated facial recognition and can have a substantial impact on its results. Illumination changes the face appearance drastically. It has been found that the difference between two same faces with different illuminations is higher than two different faces taken under the same illumination.
 
Facial recognition systems are highly sensitive to pose variations. The pose of a face varies when the head movement and viewing angle of the person changes. The movements of the head or differing POV of a camera can invariably cause changes in facial appearance and generate intra‐class variations making automated face recognition rates drop drastically. It becomes a challenge to identify the real face when the rotation angle goes higher. It may result in faulty recognition or no recognition if the database only has the frontal view of the face.
 
Occlusion means blockage, and it occurs when one or other parts of the face are blocked and the whole face is not available as an input image. Occlusion is considered one of the most critical challenges in the facial recognition system. It occurs due to beard, mustache, accessories (goggle, cap, mask, etc.), and it is prevalent in real-world scenarios. The presence of such components makes the subject diverse and hence making the automated face recognition process a tough nut to crack.
 
Face appearance/texture changes over a period of time and reflects aging, which is yet another hurdle in the facial recognition system. With the increasing age, the human face features, shapes/lines, and other aspects also change. It is done for visual observation and image retrieval after a long period. For accurate checking, the dataset for a different age group of people over a period of time is calculated. Here, the recognition process depends on feature extraction, basic features like wrinkles, marks, eyebrows, hairstyles, etc.
 
The face is the most essential part of the human body, and its unique features make it even more crucial in identifying humans. Various algorithms and technologies are used worldwide to make the face recognition process more accurate and reliable. The applications of this ever-growing technology are also expanding in healthcare, security, defense, forensic, and transportation, requiring more accuracy. However, some challenges are ubiquitous while developing face recognition technology such as pose, occlusion, expressions, aging, etc, which have been discussed above in the article.

The future of facial recognition

In a novel called 1984, George Orwell’s protagonist had continuously been uneasy because of his inherent fear that the ‘big brother’ is watching. When the story was first published in the middle of the last century, the technology needed to monitor citizen’s coming and going hadn’t even been born, but now about 7 decades later, it is everywhere.
 
A major cog in that technology is facial recognition. Backed by the proliferation of high-resolution cameras in public spaces, robust artificial intelligence algorithms, and ever-increasing computer processing capabilities, this has quickly become entrenched in our everyday lives. It wouldn’t be unfair to assert now that facial biometrics will go on to become the preferred biometric benchmark because it is easy to deploy as well as implement and requires no physical interaction by the end-user. Moreover, face detection and face match processes for identification or verification are speedy.
 
Biometrics has the power to minimize security risks, greatly improve the customer experience, and can be “the way to secure payment methods, online/eCommerce payments and a wide range of financial applications.” Furthermore, with rising regulatory pressure, biometrics can be the way to protect customers’ financial data more easily and securely.
 
As technological advancement follows high-tech expertise and is seen percolating down to different echelons of society, it also reigns supreme and becomes the keyword, and has become a must for all businesses of all kinds to take on the growing challenges of newer digital areas. Keeping up with such trends is the advent of facial recognition, which uses AI & Machine Learning, and has worked wonders in the use of security systems in addition to becoming extremely useful as a commercial identification and marketing tool.
 
In its 2019 facial recognition market report, Allied Market Research reckoned that the total value of the global sector will reach $9.6 billion by 2022 with a compound annual growth rate of 21.3% from 2016 onwards. Investment in more sophisticated types of 3D facial recognition devices has been accounting for some of that growth as retailers and others have been looking to increase returns on investments by arming existing security systems with facial recognition capabilities. By assessing customers’ facial expressions and even bodily responses, retailers are able to gain better insights into consumer behavior, even to the point where they can predict how and when a buyer might purchase their products in the future, which will help increase sales.
 
As with all new technology, the uses of facial recognition are multiplying in both predictable and surprising ways. But it’s increasingly clear that a great many of these uses have created many new and positive benefits for people around the world.
 
It’s striking to review the breadth of this innovation. Police in New Delhi recently tried facial recognition technology and identified almost 3,000 missing children in four days. Historians in the United States have used the technology to identify the portraits of unknown soldiers in Civil War photographs taken in the 1860s. Researchers successfully used facial recognition software to diagnose a rare, genetic disease in Africans, Asians, and Latin Americans. And in October last year, the National Australia Bank designed a proof of concept to enable customers to withdraw money from an Automatic Teller Machine using facial recognition and a PIN.
 
By deploying our high-tech identification matching technologies proactive and preventive measures become possible to better secure and manage critical areas while creating coordinated and attentive customer service-oriented experiences that are aimed to please. The cutting-edge facial recognition technologies by CP PLUS are ideally suited to fortify safety and security anywhere people gather while creating an environment that fulfills the expectations of customers through highly adaptive encounters.
 
The future of facial recognition technology is bright. Forecasters opine that this technology is expected to grow at a formidable rate and will generate huge revenues in the coming years. Security and surveillance are the major segments that will be deeply influenced. Other areas that are now welcoming it with open arms are private industries, public buildings, and schools. It is estimated that it will also be adopted by retailers and banking systems in coming years to keep fraud in debit/credit card purchases and payments especially the ones that are online. This technology would fill in the loopholes of a largely prevalent inadequate password system. In the long run, robots using facial recognition technology may also come to foray. They can be helpful in completing tasks that are impractical or difficult for human beings to complete.


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