Anywhere in the world, large public gatherings are a major security challenge for law enforcement officers and the event organizers. With the rapid growth of urban population and ongoing pandemic-related threats, crowd management or measuring crowd density is becoming even more important with each passing year.

At the same time, the evolution of accurate facial recognition technology and its application in crowd management offers a smart opportunity for businesses to leverage crowd density data for enhancing their business value. This is already being done by many sporting and entertainment centers, and shopping malls across the world. These establishments are deploying crowd management solutions to measure footfalls and use the data for improving customer experience (CX).

If you are wondering how face detection and recognition can help in crowd management or be used in crowd counting and footfall analysis, then here’s the answer:

How face recognition can help in crowd management?

For facial recognition technology, detecting multiple faces in a dynamic and uncontrolled environment such as a shopping mall, sporting event or religious ceremony comes with its share of challenges. Some of them include a high rate of occlusion due to heavy crowd density, insufficient lighting conditions and inadequate body positions, making it difficult to capture the required facial features.

Multiple face detection and recognition is a complex technique which uses a variety of methods and algorithms to arrive at accurate results. While the “scanning window” method is efficient to localize an independent object or a human being, other face detection methods use techniques that exploit the scene context and the relationship among objects for improved detection.

Let us look at how Airface face recognition technology works in the following areas:

  • Crowd counting
  • Footfall analysis
Facial recognition in crowd counting

Be it at large sporting events, amphitheater shows or art exhibitions, facial recognition is effective in controlling crowd and ensuring overall security. For example, at the 2019 Brit Awards show, facial recognition technology was successfully used to screen the incoming guests at multiple entrances of the event premise. The face recognition tool was also linked to a mobile app which enabled the event security staff members to verify the identity of each attendee.

Apart from tracking the event attendance, facial recognition on mobile phones can also be used to pinpoint and identify misbehavior among attendees. Facial biometrics is also being considered to automate entry-and-exit at major events which are attended by thousands of people. Facial sensors can measure the crowd density at different events and determine the factors such as ticket discounts that are driving the main traffic.

How does facial recognition work in criminal investigations? Let us explore further in the following sections.

For instance, the Major League Baseball (MLB) is working towards introducing biometric-based ticketing at their baseball games in the future. With the aid of facial recognition technology, baseball fans in the future can simply gain entry to their favorite game after getting their face scanned. Further, fans can use the same system to order and pay for their refreshments. In addition, the MLB is also pondering over using face detection technology to monitor if attendees are wearing face masks or not.

Facial recognition in footfall analysis

Customer footfalls are the driving force for any large shopping mall or supermarket. Through the process of capturing a face at its entry point, retail businesses can get answers to pertinent questions such as:

  • How many footfalls is a particular store attracting on a given day?
  • How many times has a specific customer visited the store?
  • What time of the day do stores attract the most footfalls?
  • How does the footfall-related data vary for each day, month, week or year?

Further, footfall analysis can be used to categorize your customer base using the following criteria:

  • Gender – if your business is attracting more male or female customers.
  • Age group – what is the average age group of customers who visit your business store.
  • Timings – what time of the day does your business attract the most footfalls.
  • Duration – how much time do customers of a particular age or gender spend in your store.

Among the largest malls in Helsinki, Finland, the Kamppi Shopping Centre with over 100,000 daily visitors used footfall analysis to determine when the mall attracted the most customers during the day. After their analysis, they found most customers shopped during their lunch break–as against their perception that most footfalls were during the evening.

Conclusion

Besides large events and shopping centers, crowd management solutions can also be used at individual retail shops, restaurants and even theme parks to prepare your customer servicing executive for an increase in calling customers. As an example, restaurants can be prepared to serve the favorite menu of a specific age group at a specific time slot, all based on the footfall analysis results.

Since its inception in 2009, Airface has been in the business of providing contact-free face recognition tools to a wide range of establishments such as shopping malls, retail chains and individual stores. With technology capabilities like liveness detection and multi-face identification, Airface products are designed to enable effective crowd management using face recognition.

Want to explore how to manage or extract valuable insights from large crowds? Raise a request for a free demo on our website today.

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