Facial Recognition to Prevent Shoplifting in Retail Stores

Facial Recognition: How Does It Safeguard Retail?

Facial recognition technology employs machine learning and artificial intelligence, which enhances the precision and speed of identifying potential fraudulent activities. 

By leveraging facial recognition, retail establishments reduce dependence on labor-intensive manual reviews and rigid rule-based systems. This technology empowers retailers to enhance security measures, mitigate risks, and foster a more secure and trustworthy shopping environment for customers. It provides the following benefits for the retail sector:

1. POS fraud prevention

Biometric identification provides proof of attendance, holds employees accountable for transactions, and minimizes fraud at the POS (Point of Sale). With POS system integration, employees only have to scan a finger or face – without complicated passwords or annoying ID cards.

How it works: 

• Upon initiating a transaction, the employee is prompted to authenticate their identity using biometric identification, such as facial recognition or fingerprint scanning, at the POS terminal.
• Once the employee’s identity is verified, the system validates the transaction details against the employee’s authorized privileges and access levels stored in the database.
• The system generates an audit trail that records the employee’s identity, transaction details, and timestamp, providing a comprehensive record of all POS activities for accountability and fraud detection purposes. This audit trail serves as proof of attendance and ensures that employees are held accountable for their transactions, minimizing the risk of fraudulent activities at the POS.

2. Verification of self-service checkouts

Biometric data simplifies and protects processes at self-service checkouts by handling transaction verification – even for items with age restrictions – thus shortening customer waiting times.

How it works:

• Customers scan their selected items at the self-service checkout terminal as usual.
• After scanning their items, customers are prompted to authenticate their identity using facial recognition technology integrated into the self-service checkout system.
• Upon successful facial recognition authentication, the system verifies the customer’s identity against their profile and age restrictions stored in the database. If the customer meets the necessary criteria, the transaction is authorized, and the customer can proceed with payment and exit the store.

3. ​​Shoplifter identification

This feature contributes to retail crime prevention. Retail companies have used this technology for years to scan known or suspected shoplifters, identify them, and notify security.

How it works:

• Security cameras equipped with facial recognition technology continuously scan the faces of individuals entering the retail establishment or moving within the premises.
• The facial recognition system compares the facial features captured by the cameras in real-time against a database of known or suspected shoplifters. This database may include images from previous incidents, law enforcement databases, or internal watchlists.
• If a match is found between the facial features of an individual and those in the database of known or suspected shoplifters, the system generates an alert to notify security personnel. Security staff can then take appropriate action, such as monitoring the individual’s movements, approaching them for questioning, or preventing them from leaving the store with stolen merchandise.

Let’s look at a few real-life examples of how big brands use facial recognition in retail and other industries:

1. Amazon Go stores has been utilizing facial recognition technology in its cashier-less Amazon Go stores. Customers enter the store by scanning a QR code in the Amazon Go app, and then the store’s system tracks their movements and purchases using a combination of cameras and sensors, including facial recognition technology. This system helps to prevent shoplifting by identifying customers and linking their actions to their Amazon accounts, ensuring they are charged for items they take from the store.
2. Walmart has been testing facial recognition technology to deter theft and improve customer service. In some locations, Walmart has implemented systems that use facial recognition to identify known shoplifters or individuals banned from their stores due to previous incidents. By recognizing these individuals as they enter the store, Walmart can take proactive measures to prevent theft and maintain a safer shopping environment for customers.
3. Alibaba’s «Smile to Pay» feature has introduced a facial recognition payment system called «Smile to Pay» at some of its physical retail locations. Customers can complete transactions by simply smiling into a camera-equipped point-of-sale device, which captures and analyzes their facial features to verify their identity. This technology not only provides a convenient and secure payment method but also helps prevent theft and fraud by ensuring that only authorized individuals can complete transactions using the Smile to Pay feature.

1. Customer service

• Standartized personalized assistance: Facial recognition helps to identify returning customers and personalize their service experience based on past interactions and preferences.
• Ongoing customer behavior analysis: Facial recognition tools analyze customer emotions during interactions, enabling real-time adjustments in service delivery and promptly addressing concerns.
• Customized loss prevention strategies: Facial and object recognition acts as a shoplifting deterrent.
• Improved customer experience: Customers become loyal when retailers provide a superior experience with safe, personalized services. Behavioral analysis in retail also allows for marketing campaign personalization. 

2. Streamlining operations

• Employee authentication tool: Integrate facial recognition into employee authentication systems. Retail safety measures will streamline access to restricted areas and secure sensitive data.
• Manual tasks automation: Facial recognition automates routine tasks such as attendance tracking, employee scheduling, and time management.
• Workflow optimization: The analysis of facial recognition data helps identify workflow bottlenecks. Real-time monitoring will ensure smoother operations and reduce resource wastage.

3. Inventory management

• Daily stocks monitoring: Object recognition system monitors shelf inventory levels and detects stock shortages or misplaced items, enabling timely restocking and inventory management.
• Advanced demand : Analyze facial recognition data to forecast demand accurately by identifying customer preferences and buying patterns. Most automatic matches yield high confidence scores, typically over 99%. Lower confidence scores can help identify potential matches, which human investigators further evaluate.

Data engineering services can ensure the implementation of secure and efficient data pipelines, data storage solutions, and data governance frameworks, crucial for managing the large volumes of diverse data generated by facial recognition systems. Ask Lightpoint’s experts, who have a lot of practical experience in tailoring software to business needs in the most commercially viable way.

Despite the breadth of application of the technology, there is one difficulty they all have to face – the ethical issue of recognizing individuals’ personal identities.

Ethical Question of AI Utilization For Facial Recognition

The «Ban Facial Recognition» campaign, supported by over 40 organizations, including fundamental rights NGOs, compiles a list of companies known to utilize facial recognition, along with those where its usage remains ambiguous or could be anticipated in the future. Additionally, the list includes companies that have explicitly communicated their refusal to employ this technology. 

The questions raised regarding ethical AI utilization for facial recognition in retail include:

• How can retailers ensure biometric data protection? Will retail security solutions respect customers’ privacy rights?
• How can retailers address and mitigate biometric authentication algorithms’ potential biases and discriminatory outcomes, especially concerning race, gender, age, and other demographic factors? 
• How can retailers ensure transparency after security system integration and obtain informed consent from individuals whose facial data is collected and processed? How can they provide clear information about how the data will be used and shared?

Important to mention that many shoppers are not even aware of the use of the software, as a representative survey by market researchers at Piplsay showed. Of around 31,000 people surveyed, 40 percent were unaware that some stationary retail companies use the technology. 38 percent are against it, 42 percent don’t care. Around 65 percent want an opt-out option to speak out against the use of such identification.

For now, the general public does not seem to have fully grasped how far mass surveillance has reached in the United Kingdom. When the Daily Mail revealed in the spring that Sports Direct, one of the largest sports chains, was using the Facewatch system, it caused an absolute scandal. Dozens of members of parliament protested, and a widely supported open letter from the fundamental rights organization Big Brother Watch warned about the technology.

Another issue occurred in Australia. The Data Protection Authority decided in October 2021 that the biometric data had been collected without the consent of those affected – and was not necessary for the survey. In that case, signs in stores and data protection regulations available online referred to facial recognition. However, the data protection authority did not consider this to be sufficient.

7-Eleven had already deactivated facial recognition before the authority’s decision. The company was also ordered to delete the stored data.

Therefore, to balance security with customer privacy and ethical use of software, AI-driven tools in retail must adhere to the following principles:

1. Data minimization: AI-driven software should only collect and retain the minimum amount of data necessary for its intended purpose. This helps reduce the risk of unauthorized access or misuse of sensitive customer information.
2. Anonymization and pseudonymization: Personal data should be anonymized or pseudonymized wherever possible to protect customer privacy. This involves removing or encrypting personally identifiable information to prevent the identification of individuals without authorization.
3. User consent and transparency: AI-driven software should obtain explicit consent from users before collecting or processing their data. Additionally, the software should provide clear and accessible information about how data is used, shared, and stored to ensure transparency and build customer trust.
4. Algorithm fairness and accountability: AI algorithms must be designed and trained to mitigate biases and ensure fairness across different demographic groups. Retailers should regularly evaluate and monitor their AI systems to detect and address any instances of bias or discrimination. Furthermore, retailers should implement mechanisms for accountability, allowing users to understand and challenge algorithmic decisions if needed.

Therefore, AI-driven software tools can strike a balance between security, customer privacy, and ethical use in the retail sector. To avoid problems related to user data privacy violations, you can apply for AI software development services from a company with relevant industry experience.

• Deep learning and neural networks. Deep learning algorithms have revolutionized facial and object recognition capabilities. They will become indispensable physical security enhancements as they identify and classify objects, individuals, and suspicious activities in real time. With improved facial recognition, shoplifting will continue to decline.
• 3D facial recognition: 3D facial recognition systems capture and analyze facial expressions and reactions to product displays, advertisements, and promotions. Thus, retailers gain valuable insights into customer preferences and can offer them tailored shopping experiences.
• IoT integration: Integrating facial and object recognition capabilities into IoT (Internet of Things) devices such as security camera networks allows for real-time data processing. For example, the connected devices ecosystem will automatically call for the police in an emergency.

These emerging technologies are driving innovation and expanding the capabilities of facial and object recognition systems, paving the way for enhanced security and convenience across the retail industry.

Conclusion

While governments are currently debating whether and to what extent biometric real-time surveillance should be permitted, the practice has become part of everyday life in retail. All in all, AI-driven facial recognition is utilized in various retail areas to enhance security, personalize user experiences, and streamline operations:

• at store entrances and exits to identify known shoplifters or individuals with a history of theft, thereby deterring potential theft and minimizing losses,
• during self-checkout to verify the identity of customers and ensure that items scanned match those being purchased, reducing the likelihood of theft or fraudulent transactions,
• in areas where high-value merchandise is displayed, triggering alerts if individuals spend an unusual amount of time near these items or if they attempt to conceal them.

Protect yourself at the door and help combat shoplifting and fraud using the best-suit software solutions. To ensure ethical and effective recognition implementation, give us a call and get expert guidance with project planning.