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Top 4 Facial Recognition Data Collection Methods in 2024

Top 4 Facial Recognition Data Collection Methods in 2024Top 4 Facial Recognition Data Collection Methods in 2024

Despite the controversies surrounding this technology, the market for facial recognition systems (FRS) continues to grow1. Facial recognition applications are seen everywhere, from helping improve mental disorder diagnoses to finding fugitives. Developing and improving these systems requires facial data, which sometimes can be difficult to obtain due to security and privacy-related concerns of people.

Choosing the right method is a crucial step in simplifying the facial recognition data collection process; therefore, we have curated this article to explain:

  • What data collection means for facial recognition systems
  • What are the top 4 methods of collecting facial data
  • And our recommendations on how to select the right method for your project.

What does data collection mean for facial recognition systems?

We have discussed data collection and how it fuels AI/ML models before. Facial recognition is a branch of image recognition that also functions through AI/ML models. Data collection for facial recognition falls under image data collection and involves gathering face images to train and improve FRSs.

Face images are taken of different people, annotated, and then fed into a machine learning model, which uses them to learn how to scan, identify and process facial features.

The basic steps included in in-house face image data collection:

  1. Understand the scope of the project (what kind of faces it will scan, the size of the population it will be deployed on, etc.)
  2. Recruit participants
  3. Take images based on the requirements of the project
  4. Organize and pre-process the images based on attributes

What are the top 4 methods of collecting face image data?

There are different methods of collecting data for facial recognition systems. Here, we identify the most commonly used ones.

1. Prepackaged/Public face image dataset

These datasets are created by a third party and are ready to use after purchase. Prepackaged datasets are also sometimes available for free, like the CelebA dataset2.


  • Easy to access: They are readily available online and can be downloaded quickly
  • Cheaper: They are more affordable than in-house image data collection and sometimes free to use.


  • Lack of customization: The dataset is not unique to your project requirements. For instance, if the dataset does not have images with a certain type of obstruction on the face, such as masks or glasses, then additional data needs to be added, which can add preprocessing costs to the budget.
  • Quality level: The quality can be low since such datasets (especially public datasets) do not go through rigorous quality checks and are collected by the general public. 

2. Face image data collection through crowdsourcing 

The crowdsourcing method includes working with the public to gather fresh face image datasets. If done in-house, the company needs to develop an online platform that will allow the crowd to register, get data collection projects and submit the data in exchange for compensation.


  • Scalable and customizable: You can customize and scale face image datasets by specifying your requirements to the service provider.
  • Cheaper: Since the crowd uses their cameras/smartphones, this method becomes cheaper than other in-house collections. 
  • More diverse data: The image dataset collected with crowdsourcing is more diverse (Images of people with different skin colors, hairstyles, ages, etc.) since a larger number of contributors can be reached worldwide.
  • No copyright issues: When the crowd registers through the platform, they agree to the terms and conditions, which transfer the rights of the image data to the company.


  • Quality issues: Since the images are collected by the contributor’s own smartphones/cameras, the quality can get affected. For this, the company needs to provide clear and comprehensive quality instructions or spend extra effort in preprocessing the images collected by the crowd.
  • Platform cost: Crowdsourcing is done through an online platform (usually a mobile application); developing or purchasing such software can add extra time and costs to the process.

3. Automated face image data collection

Image data for facial recognition systems can also be automated by integrating machine learning into the process. This can be done through web scraping or crawling, where the data is scraped from specified or unspecified online sources.


  • No human input: This does not require human input after the initial setup.
  • Does not get tired: Can collect face images non-stop without human errors.
  • Cheaper: It is cheaper since no equipment or recruitment of contributors is required.


4. In-house face image data collection

This method involves creating a separate image data collection project to develop a facial recognition system. The team must purchase cameras and other lighting equipment and hire contributors to take their images.


  • Higher data protection: Good for projects that are confidential in nature. For instance, a facial recognition system is being used by the FBI or the government3.
  • More control: The data collection process can be more controlled. The team can use the cameras of their choice, the contributors of their choice, and the setting (lighting, background, angle, etc.) of their choice.
  • Complete data ownership: Since an image of someone’s face is their biometric information, getting ownership rights is important before using it. In-house image data collection allows complete ownership of the data by the company and eliminates the risk of future data-related lawsuits. This is because the contributors sign an agreement with the company during recruitment.


  • Expensive: This method is the most expensive method amongst all the others since the company covers all the expenses by itself.
  • Lack of diversity: This method limits the level of diversity in the dataset since a smaller number of contributors can be hired as compared to, for instance, crowdsourcing. 
  • Time-consuming: This method is also more time-consuming than other methods, such as using prepackaged data or working with a crowdsourcing data collection service.

Recommendations on how to choose the right method

Selecting the right method for your facial recognition project depends on your project. The following factors can be considered:

Project’s scope

It is important to consider how big your project is. For instance, if a facial recognition system is deployed in only one country, then pre-packaged or even public datasets can be used to train it. However, crowdsourcing would be a more suitable option if it will be deployed in multiple countries and requires a dataset with different people.

Project’s level of confidentiality and complexity 

Confidential projects can benefit from in-house image data collection. In contrast, projects which are not secretive or have a complicated model can use public datasets.

Project’s budget

As previously mentioned, in-house data collection can be expensive and time-consuming; therefore, if the project has time and budget limitations, then working with prepackaged datasets or crowdsourcing data collection service providers can be more suitable.

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

Get Data Collection Whitepaper

Further reading

Further reading

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  1. Sava, J, A, (Oct 6, 2022) Facial recognition market size worldwide from 2019 to 2028, Statista. Retrieved: Nov 15, 2022.
  2. Liu, Z., Luo, P., Wang, X., & Tang, X. (2018). Large-scale celebfaces attributes (celeba) dataset. Retrieved August, 15(2018), 11.
  3. Cheng. P. S., Dienst . J., Reilly. R. J, (Oct 21, 2022) Facial recognition used in arrest of 9/11 conspiracy theory lawyer accused of trying to disarm officer on Jan. 6. NBC NEWS. Retrieved: Nov 15, 2022.
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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