Can facial recognition technology be of help in preventing retail theft?

software testing in artificial intelligence

 

It is no more a secret that consumers purchasing inside stores are being watched. Whether it is a premium boutique store like Calvin Klein or a big box retailer like Walmart, overhead cameras have become a regular fixture inside stores. These cameras record the activities of shoppers to identify instances of shoplifting. However, what if these cameras continuously scrutinize the faces of shoppers through an arrangement and look for potential shoplifters? Biometric technologies like facial recognition have become more robust, accessible and advanced with the incorporation of artificial intelligence. Now, several stores are utilizing facial recognition technology to spot shoplifters. As per a survey by the National Retail Foundation, retail organizations lose about 50 billion dollars annually due to shoplifting.

Previously, facial recognition was associated with cybersecurity but today, it has an active application into other sectors as well such as marketing, retail, and health. In fact, more number of retail-based brands are looking for ways to engage customers with this technology. Many entrepreneurs are offering better shopping experiences by incorporating cutting-edge technologies such as augmented reality, CRM, facial recognition, and artificial intelligence. Thus, facial recognition has become one of the most prominent technologies to drive future stores. Let us dig deeper to find out how facial recognition can help future retail stores to succeed.

Sending tailored messages:

It is easier to track customers who are shopping online, but quite difficult for the brick and mortar stores. Here, face recognition software can enhance customer experiences by identifying VIP shoppers who check-in. Retailers can offer bespoke text messages to consumers in stores to promote discounts, recommendations, and other offers.

Preventing organized crime and violence in stores:

As per surveys, retail crime incidents cost U.S retailers around 30 billion each year and unfortunately, are getting aggravated. The loss prevention teams find it difficult to recall the names and faces of every retail criminal. However, with the help of facial recognition, retailers can proactively stop organized retail crimes. This biometric process also helps to keep stores safer. Security personnel generally react to crimes in progress without the presence of facial recognition. This AI-based technology can empower them to take a proactive approach to block crimes even before they happen.

Offering bespoke assistance during in-store purchase:

Face recognition can also be applied to convey meaningful notifications to retailers. As a result, store employees can offer better assistance to shoppers present in the store. Facial recognition technology can incorporate a wide range of retail solutions such as loyalty systems, CRM, point-of-sale and many others. Store executives can gather substantial information about customers thus helping them to do a better job of assisting customer service.

Are all facial recognition technologies up to the mark:

Facial recognition is a complex process, which needs to be tested rigorously to enable its flawless performance. AI application testing would ensure that all security tests run seamlessly. This type of retail mobile application testing must focus on comparing various sets of data to get the desired results. Presently, most facial recognition faces challenges regarding high FAR and FRR rate. Hackers too are on the lookout to exploit the technology for their nefarious activities. However, a thorough testing process would make sure the facial recognition software performs as desired. It accomplishes the moderate accuracy levels of facial recognition software by decreasing security vulnerabilities.

Challenges of facial recognition software:

According to a survey by Forrester, the biggest challenge facing a facial recognition AI system is a well-curated collection of data. FAR (False Acceptance Rate) and FRR (False Recognition Rate) are the two major challenges of AI system. FAR is known to be the ratio of false acceptances and identification attempts while FRR is known to be the ratio of false recognition and identification attempts. The impactful challenges apart from FAR and FRR to the facial recognition system are the occlusion of subjects, different expressions of an individual, movements of the head or pose variations, and background of the subject.

However, to carry out best practices, organizations need to engage in software testing in artificial intelligence to deliver foolproof results.

Conclusion

Facial recognition application is utilized by retailers to rapidly recognize offenders and inform team members to ensure in-store loss prevention. This technology helps to prevent crime before commencing. However, AI application testing remains critical to making sure that collected data remains secure and the loss prevention team can prevent any attempts by hackers. It demands a standard accuracy level that can only be attained through retail software testing.

Diya works for Cigniti Technologies, Global Leaders in Independent Quality Engineering & Software Testing Services to be appraised at CMMI-SVC v1.3, Maturity Level 5, and is also ISO 9001:2015 & ISO 27001:2013 certified.