Published on 06 Feb 2025
Facial recognition technology is a biometric technology that involves identifying individuals based on their facial features. A digital map of the face is created using unique features like distance between eyes, shape of nose etc.
Types of Facial recognition technology
Verification (1:1): This type involves comparing a person's face to a single stored template to verify their identity. It's commonly used for authentication purposes, such as unlocking smartphones or accessing secure locations.
Identification (1:N): Identification involves comparing a person's face against multiple templates in a database to find a match. It's used for scenarios like identifying individuals in large crowds or tracking suspects in surveillance footage.
Application of Facial recognition technology
Security and Law Enforcement: Facial recognition is used for access control to secure areas, border control, and identifying criminals in surveillance footage.
Authentication: Many smartphones and devices use facial recognition as a biometric authentication method to unlock the device, access apps, or make secure transactions.
Attendance Tracking: Facial recognition can automate attendance tracking in schools, workplaces, and events.
Airport Security: It's used at airports to enhance security by comparing passengers' faces to their passport photos.
Healthcare: Facial recognition can aid in patient identification and tracking in healthcare settings.
Smart City Solutions: Facial recognition can be used for traffic management, public safety, and monitoring public spaces.
Issues associated with Facial recognition technology
Privacy concerns: Facial recognition involves capturing and storing sensitive biometric information, raising questions about individuals' right to privacy and control over their personal data.
Accuracy concern: The technology also possesses error resulting in both false positives and false negatives.
Example: A study conducted by the National Institute of Standards and Technology (NIST) in the U.S. found that facial recognition systems were less accurate when identifying people of Asian, African, and Indigenous descent.
Discrimination: Biased training data can result in disproportionate misidentifications of certain racial, ethnic, or gender groups, leading to discriminatory outcomes thus reinforcing social prejudices.
Example: Amazon's facial recognition system, Rekognition, was criticized for misidentifying people of colour more frequently than white individuals.
Lack of consent: Individuals often do not have the opportunity to give informed consent for their facial data to be collected and used, especially in public spaces or by private companies.
Government surveillance: Government agencies' use of facial recognition for mass surveillance can infringe on civil liberties and citizens' freedom of expression.
Example: The use of facial recognition by the Chinese government for mass surveillance of Uighur Muslims has raised concerns about human rights abuses.
Lack of regulation and transparency: In many regions, there is lack of comprehensive regulatory mechanisms which questions the transparency of the technology.
Way forward
The facial recognition algorithm needs to be made more accurate through better testing and use of unbiased training data must be ensured. A comprehensive legislation on regulating the agencies which use these data and well laid procedures on the use of these biometric data can bring more confidence within the public regarding the technology.
Security
Cyber security
Facial recognition technology
Data protection
applications of facial recognition technology
General Studies Paper 3
Cybersecurity