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Facial Recognition: Best Practices & Use Cases in 2024

Facial Recognition: Best Practices & Use Cases in 2024Facial Recognition: Best Practices & Use Cases in 2024

The global market for facial recognition technology is rapidly increasing. The market size is projected to increase from $5 billion in 2021 to almost $13 billion by 2028. However, implementing facial recognition technology has various factors that business leaders must consider before initiating any projects. Implementing facial recognition in your business can become a complicated and difficult process without these considerations.

This article explores facial recognition, its best practices, and use cases to help business leaders make the right decisions.

What is facial recognition, and how does it work?

A facial recognition system scans and detects a person’s face from a database while matching it with a saved digital image or frame of a video clip. The technology works by pinpointing the facial features of an image and comparing them with other images from the database by using an AI algorithm (see Figure 1).

Facial recognition is a branch of image recognition and works on similar rules for identifying patterns in images. The technology also improves the performance of ML models following repeated interactions with data and images.

Figure 1. A basic facial recognition system framework

Flowchart of a basic facial recognition system

What are the best practices for using facial recognition?

This section highlights some best practices that can be considered while developing and implementing a facial recognition system in your business.

1. Ensure dataset quality while training the model

Like any AI/ML model, data collection is one of the most important steps in training a facial recognition system since it determines the end performance of the system. The developer needs to ensure that the right dataset is selected for the training process and that the model is not over/underfitting and is unbiased.

Facial recognition systems usually require large datasets to be trained to avoid false positives. The more data is fed into the algorithm, the more accurate it will become. Crowdsourcing can be an effective method of collecting large and diverse datasets for facial recognition systems.

You can also check our data-driven list of data collection/harvesting services to find the option that best suits your project.

For more in-depth knowledge on data collection, feel free to download our whitepaper:

Get Data Collection Whitepaper

2. Ensure high-quality data annotation

After gathering the dataset, annotating is required so that the algorithm knows what to look for in the image. For annotating data for a facial recognition system, different points and features of the face are tagged/labeled with accuracy and consistency.

A face of a woman with point labels on different parts of her face.

Source: Clickworker

Click here to learn more about image annotation and why it is important.

3. Consider ethical factors while implementing facial recognition

The use of facial recognition technology comes with various ethical constraints and considerations. This is because the system uses people’s photographic/biometric data, which in some countries can be illegal if certain rules and regulations are not followed. 

Considering the following can be helpful before implementing a facial recognition system in your business:

Notify people

  • Clearly notify the people about their biometric/photographic data being collected.
  • Avoid placing cameras in sensitive locations such as dressing rooms, bathrooms, medical facilities, etc.
  • Clearly disclose how and where the biometric data will be used and which parties will have access to it.
  • Acquire affirmative and explicit consent before using someone’s image or any biometric data derived from an image.
  • The consent of the parent or legal guardian must be taken for minors. 
  • Provide the consentee enough opportunities to provide consent whenever the data is used.
  • Make sure that the process of giving consent is clear and easy to understand by the consentee
  • Make sure to provide an option to revoke the consent at any given point.

Protect the data

  • Ensure proper administrative, technical, and physical protection of the data.
  • Regularly review data security policies.
  • Protect the access to computers and servers to prevent unauthorized access or unintended disclosure of the data. Protect login details and passwords.
  • Restrict access to the data.

Additionally, make sure to go through the rules and regulations specific to your country regarding biometric/photographic data gathering, using, and sharing.

This section highlights some real-world applications of facial recognition.


  • Facial recognition can automatically scan the face of the patient and extract medical history and insurance information, reducing the workload for healthcare workers and saving the patient’s time. A recent study shows a 66% percent acceptance of facial recognition technology by patients to extract their information.
  • Facial recognition can also be used to diagnose medical disorders in patients with mild or difficult-to-detect symptoms.

Watch how facial recognition is helping improve mental disorder diagnosis


  • In the retail sector, facial recognition is being used to automate B2C operations. For instance, Amazon go stores are using the technology to implement contactless payments.
  • Customer loyalty offers can also be offered through facial recognition without the customer being interrupted to give a more personalized shopping experience.
  • Retail security can also be improved through facial recognition to reduce shoplifting.

Click here to learn more about how facial recognition enabled through computer vision is used in retail.

Banking and finance

  • Customer identification and verification can be done through facial recognition.
  • Similarly, facial recognition technology can make the customer onboarding process faster.
  • To reduce ATM robberies or banking fraud cases, facial recognition can be used to add another layer of security.

The following video shows the benefits of facial recognition in the banking sector.

Law enforcement and security services

  • Facial recognition has been widely used by law enforcement officers for a while now. It is mainly used to identify and record criminals or persons of interest.
  • Facial recognition is also deployed in various surveillance cameras throughout a city, such as at junctions, train/bus stations, and airports, to detect fugitives or people of interest.

However, the use of facial recognition by governments and law enforcement agents is also widely criticized by many. 

Watch the following video to learn more:

You can also check our data-driven list of image recognition software to find the option that best suits your business needs.

Further reading

If you have any questions or need help finding the right solution/vendor for your business, feel free to contact us:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Shehmir Javaid
Shehmir Javaid is an industry analyst in AIMultiple. He has a background in logistics and supply chain technology research. He completed his MSc in logistics and operations management and Bachelor's in international business administration From Cardiff University UK.

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