Biometric access control
What Does Biometric Access Control Involve?
Biometric access control employs a person's unique chemical, behavioral, or physical attributes to establish their identity. This technology finds applications in various domains, ranging from identity management to regulating access in computer networks, financial transactions, and transportation systems. The primary goal of biometrics in these scenarios is to verify or ascertain an individual's identity to prevent unauthorized access. Unlike password-based or access card systems, which rely on information that can be forgotten or lost, biometrics relies on inherent attributes, determining access based on individuals' intrinsic characteristics.
In essence, a biometric access control system operates as a pattern recognition mechanism. It captures specific biometric data from an individual, focuses on pertinent features of that data, compares them with a pre-established set of attributes stored in its database, and triggers actions based on the accuracy of the comparison. Various characteristics, such as fingerprints, irises, hand geometries, voice patterns, or DNA information, can serve as biometric points of comparison. While certain limitations exist, an effective system can precisely identify an individual based on these traits. A standard biometric access control system comprises four primary components: a sensor device, a quality assessment unit, a feature comparison and matching unit, and a database.
Sensor Device in the Biometric Access Control System
A biometric reader or scanning device is employed to gather the requisite verification data from an individual. For instance, in fingerprint biometrics, an optical sensor captures an image of the fingerprint's ridge structure, forming the foundation for subsequent access control activities. The sensor unit serves as the critical interface between users and the biometric access control system, emphasizing the need to minimize reading failures. The quality of data obtained through sensors often depends on camera characteristics, as most biometric data comprises images, except for audio-based systems like voice recognition and chemically-based systems like odor identification.
Assessing Data Quality
In an access control system, the collected biometric data from the sensor device undergoes evaluation to determine its suitability for processing. Signal enhancement algorithms are often applied to improve data quality. If the quality remains inadequate for processing, users might be prompted to re-submit the data. After processing, specific features are selected from the overall dataset to represent the qualifying identity trait. For example, in fingerprint scanning, the relative positions of small ridge points can be extracted as a biometric measure. This feature set used for assessment and extraction is referred to as the biometric template, stored within the system's database.
Comparison and Matching
Once the feature set is extracted, it is compared with stored templates to find matching points. The number of matching points yields a match score, which can vary based on the quality of collected data. The matching component in a biometric system often incorporates a decision-making mechanism that uses the match score to either confirm an individual's identity or correlate the score with a ranked list of potential identities in the database.
The Biometric Access Control System Database
The system database stores all the necessary information for processing biometric readings. During access control configuration, the feature template is input into the database, sometimes accompanied by user-specific biographical information to enhance security. These setting and data gathering processes can be automated or supervised by a technician. For instance, for personal computer access control, users may input their own data to safeguard their resources. However, in restricted facilities, managerial authorization or supervised input might be required to establish biometric controls.
While a single biometric sample can suffice for extracting a user template, some systems process multiple samples to create a mosaic representation. Some systems also store multiple templates to accommodate data variations from a single user. This approach is commonly seen in facial recognition biometrics, where multiple templates account for facial pose variations relative to the sensor.
Mark Vena
Senior Director, Business Development
Past Industry Experience: As a technology industry veteran for over 25 years, Mark Vena covers many consumer tech topics, including PCs, smartphones, smart homes, connected health, security, PC and console gaming, and streaming entertainment solutions. Mark has held senior marketing and business leadership positions at Compaq, Dell, Alienware, Synaptics, Sling Media, and Neato Robotics.