How Does Facial Recognition Work? (Updated for 2025)

Have you ever met someone really good at recognizing others based on their facial features? Some people can even remember the faces of others they’ve only met once before.

Facial recognition takes this concept to the next level by turning a picture of a face into data. Facial Recognition Technology (FRT) is exactly what it sounds like — tech that recognizes faces. But how does it work? Behind the scenes, there’s a complex process of analyzing facial features, converting them into numerical data and then matching them to a database.

How Technology Recognizes Faces

FRT parses out the structure of a face and makes a map from the geometry it measures out. Here’s a more detailed breakdown of how this process works:

• Detection: The first step involves detecting a face in an image or video feed. This is typically done using machine learning algorithms that identify facial features within a given image, even in crowded or low-quality pictures.
• Landmark detection: After detecting a face, the system identifies key facial landmarks. These can include the eyes, nose, mouth and jawline. More advanced algorithms can also track smaller features, such as the curve of the chin, the width of the nose and the position of the ears.
• Measurement of distances and ratios: The system measures various distances and ratios between these landmarks. For example, the software calculates the distance between the eyes, the length of the jawline and the distance from the forehead to the chin.

• Creating a faceprint: These measurements are used to create a unique “faceprint,” a mathematical model that represents the specific facial structure. This faceprint is different from a photograph because it represents the geometry of the face, turning it into a set of numerical data that the system can compare and analyze.
• Feature extraction: The system may also use techniques such as Principal Component Analysis or deep learning models to extract and refine important features of the face. This process focuses on minimizing irrelevant details while maximizing the characteristics that are most useful for identification, like the shape and relative positions of facial features.
• Encoding the faceprint: Once these measurements are extracted and processed, the face is encoded into a vector — essentially, a digital “signature” that can be stored or compared against a database of known faceprints. 
• Comparison and matching: When facial recognition software is tasked with identifying or verifying an individual, the system compares the current faceprint to those in a database. If a match is found, the software can either confirm the person’s identity or flag it for further review.

This combination of geometry-based measurements and machine learning models allows FRT to work with a high degree of accuracy. However, the effectiveness of the system still depends on various factors, such as the quality of the image, lighting conditions and the sophistication of the algorithms used.

Who Uses Facial Recognition?

FRT is used across several industries today:

• Government and law enforcement: FRT is used for surveillance, criminal investigations and border control.
• Airports: FRT helps speed up passenger processing, baggage handling and security.
• Retail: FRT is used for customer personalization, security and targeted advertising.
• Consumer electronics: FRT is used in smartphones and devices for secure authentication and personalized experiences. For instance, Apple’s facial recognition software marks 30,000 infrared dots on a person’s face to unlock an iPhone.
• Healthcare: FRT helps with patient identification, access control and improved security.
• Schools: FRT is used to track attendance and monitor campus security.
• Finance: FRT has become a staple in mobile banking and ATMs for identity verification and fraud prevention.
• Public spaces: FRT is used in events for ticketing, security and crowd control.
• Military and defense: FRT aids surveillance and helps identify targets in conflict zones.
• Transportation: FRT is used for in-vehicle access, driver monitoring and smart city initiatives.

How Accurate Is Facial Recognition?

FRT has made significant strides in accuracy, with some systems now achieving accuracy rates exceeding 90%. However, these outcomes are not universal due to challenges such as unbalanced datasets, where certain demographic groups are underrepresented in training data. This imbalance can lead to reduced accuracy for underrepresented groups. 

Recent research has focused on mitigating these biases. For instance, researchers at NYU Tandon School of Engineering developed a method to generate demographically diverse synthetic face datasets, which can train facial recognition AI models to produce more equitable results across different racial groups. 

Despite these advancements, concerns about bias and fairness persist. While some FRT algorithms perform well, they still show a tendency for false positives, especially among certain demographic groups, such as people of East Asian descent, Black people — particularly Black women, older adults and women in general. 

While FRT has become more accurate, challenges related to bias and fairness remain. Ongoing efforts are essential to enhance the accuracy and equity of these systems.

The Public Opinion

As of 2025, public opinion on FRT remains divided. While some people support its use in specific areas like law enforcement and security, concerns about privacy and surveillance persist. 

A 2021 survey found that 46% of Americans support FRT for public safety, but 75% of people remain wary of its broader use. This concern is especially high regarding potential misuse by governments or corporations.

In response, there are calls for clear regulations and transparency in FRT deployment, especially within law enforcement. The Biometrics Institute has urged for ethical guidelines to build public trust. Internationally, there are varying opinions, with some countries, like Australia, raising alarm over the lack of consent and awareness regarding the widespread use of facial recognition.

Overall, while FRT shows promise for specific applications, its widespread use continues to raise significant concerns about privacy and security.

The Future of Facial Recognition

The future of FRT holds great potential, but its widespread use will depend on addressing concerns about privacy, accuracy and bias. As the technology evolves, balancing its benefits with responsible regulation and ethical practices will be key. Moving forward, ongoing discussions and advancements will shape how facial recognition is integrated into society, ensuring its use remains both effective and fair.

Original Publish Date 6/12/2019 — Updated 2/27/2025