XHS Implementation & Limitations
Merits and Demerits of Biometric Identification
The use of biometrics, the science of analyzing unique physical or behavioral characteristics to recognize individual identities, offers numerous benefits. However, it also carries certain risks, as outlined below.
Table 2: Merits and Demerits of Biometric Identification
Merits
Demerits
High security and accuracy: Unlike passwords, biometric data cannot be forgotten.
Requires integration and/or additional hardware.
Simplicity and convenience: This ease of use contributes to the growing popularity of biometric authentication.
Potential delays: Some biometric recognition methods may take longer than expected.
Enhanced authenticity: Biometric data provides a higher level of authenticity, especially for users prone to weak or easily shared passwords.
Accessibility issues: Not everyone can participate in the biometric enrollment process due to physical disabilities.
Affordability: Biometric authentication is now available on a wide range of common devices.
Trust concerns: Users must trust that their biometric provider keeps their data secure and private.
Flexibility: Users carry their own security credentials, eliminating the need to memorize complex passwords.
Trustworthiness: Reports from 2021 indicate that younger generations trust biometric solutions more than others.
Time-saving: Biometric solutions conserve time.
In summary, while biometric identification offers significant advantages in terms of security, convenience, and user trust, it also poses challenges related to integration, potential delays, accessibility, and data privacy.
Active & Passive Liveness Testing
Enterprises utilize face recognition for onboarding, validating, and approving customers due to its reliability and ease of use. The demand for liveness detection is rapidly increasing. Liveness detection identifies presentation attacks like photo or video spoofing, deepfakes, and 3D masks, rather than merely matching facial features.
This technology makes it significantly harder for adversaries to spoof identities. While facial recognition verifies whether the person is unique and consistent, liveness detection ensures the person is a living human being. It confirms the presence of a user's identification credentials and their physical presence, whether on a mobile phone, computer, tablet, or any camera-enabled device.
There are two methods in facial liveness detection: active and passive.
Active Liveness Detection Active liveness detection requires the user to perform an action to confirm they are alive. Users may be asked to change head positions, nod, blink, or follow a mark on their screen with their eyes. However, fraudsters can sometimes deceive active methods through presentation attacks, using various gadgets or "artifacts."
The XSTAR active liveness detection model asks the user to turn their face left or right, blink, and display emotions such as happiness, anger, or surprise to determine authenticity.
Passive Liveness Detection does not require any user action, providing a modern and convenient experience. It determines if a live person is present without specific movements or gestures, using a single image analyzed for multiple characteristics.
The XSTAR passive liveness detection model assesses if a live person is present through:
Texture and Local Shape Analysis: Evaluates the image based on texture analysis, image quality, characterization of printing artifacts, and differences in light reflection.
Distortion Analysis: Uses the Image Distortion Analysis (IDA) feature vector, which includes specular reflection, blurriness, chromatic moment, and color diversity.
Edge Analysis: Examines the edges of the input image to determine if edge components are present.
In summary, both active and passive liveness detection methods enhance the security of face recognition systems, making it challenging for fraudsters to bypass these technologies.
Face attributes evaluation
Find out the estimated age range of a person, neutral or emotional facial expression, the presence of a beard, smile, glasses, sunglasses, head covering, medical mask, headphones, etc. with the help of AI-powered face attributes recognition. Create a customized process scenario for further regulation.
Face Image Quality Assessment
Ensure a photo meets industry standards, such as ICAO 9303, ISO/IEC 19754, and TR 29754, by evaluating it against 45 customizable quality parameters. These include head size and position, pose and expression, face occlusion, and the presence of inappropriate objects. This tool is ideal for verifying photos for enrollment purposes.]
Face Matching
Verify if the same person appears in different photos. In a single request, XSTAR can match a portrait to another reference photo, whether it's from a printed document, RFID chip, a selfie from a web or mobile device, or an external database.
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