Digital onboarding is the process by which new users initiate their use of a service or open an account online. While this process may be straightforward for certain services, such as online shopping, it becomes more complex for industries like banking that require identity verification to comply with regulations. In such cases, the digital onboarding process encompasses online identity verification.

This entails confirming that the individual’s identity aligns with the information provided in their documents, a task that historically necessitated in-person checks. However, advancements in technology, particularly in face biometrics, now enable users to securely verify their identity online by scanning a government-issued ID and their face. This method offers convenience without compromising security.

This digital process mirrors the traditional method employed by authorities in person. For instance, a police officer or clerk would compare the photo on your ID with your physical appearance to confirm your identity. Biometric facial recognition algorithms replicate this process, but with greater accuracy and speed. The computer analyzes the photo on your ID and compares it to a selfie you provide, a procedure technically referred to as 1:1 verification, where one photo from your ID is matched against one from your selfie. This innovative approach grants access to various services that were previously restricted to in-person interactions, all from the comfort of your home or while on the move.

TruCrowd system is equipped to handle up to 1000 types of identification documents, utilizing advanced algorithms to verify non-standard and foreign IDs efficiently.

We consider an identity document acceptable for face ticket registration if it is an official document that proves a person’s identity. This is done by providing the person’s face and their name and other details that uniquely identify them.

Data needed to uniquely identify a person:

• Face photo
• Name (full name whether in one field or split into multiple fields)
• Date of birth
• Additional identifiers needed, like personal number, parent’s names, etc.

Data needed for a unique ID document:

• Document unique number
• Date of document expiry (There are existing documents that do not expire, but this practice is now avoided by governments, as the photos need to be updated on the documents regularly to cope with a person’s aging. Old documents without expiration can be supported but may have downsides.)

TruCrowd supports identity documents of the following types:

• Passports
• Identity cards
• Driving licenses
• Foreign permanent residence cards
• and other cards of similar format containing a photo of the holder

The support for document recognition may be in two levels:

• Level 1 means extraction of the data found in the MRZ zone.
• Level 2 means extraction of all the data in both the visual inspection zone (VIZ) and MRZ zone (if present).

For Level 2 support, the DOT Document Server needs to be trained to support each individual document type and its edition. 

Please check the availability of the required document in the list of supported documents for both levels. 

In the case an ID document type required for your project is not mentioned in the list, a future version of the Document Server can be trained to support it. Please contact TruCrowd to request it.

Documents not containing an MRZ field and not supported by Level 2 are classified as unknown documents.

Liveness detection is a vital process that ensures the person validating the face ticket online during digital onboarding is indeed the individual they claim to be. By verifying that the selfie is captured from the person purchasing the ticket and not a simulated representation (such as a photo, video, or mask), liveness detection enhances security and authenticity.

Powered by AI, liveness detection algorithms distinguish between genuine interactions by live human beings using real faces and fraudulent attempts, such as spoof attacks or presentation attacks.

This method securely determines whether a biometric sample, like a selfie, originates from a legitimate human source or from a deceptive representation, such as a photo, video, or mask.

With the capability to discern live individuals from presentation attacks, which may involve masks, photos, or videos, liveness detection plays a crucial role in identity verification processes across various sectors, including banking, telecommunications, eGovernment, and the sharing economy.

TruCrowd’s solution employs liveness detection by Innovatrics. The effectiveness of various liveness algorithms can be assessed through tests conducted by the National Institute of Standards and Technology (NIST). The latest NIST FATE PAD report details the evaluation results of 82 algorithms from 45 developers tested for two specific use cases: impersonation and evasion.

The fan identity verification algorithm will not find a sufficient match if the face image on the document does not align with the selfie. If a buyer attempts to alter the selfie with a deepfake or uses printed or digital photos, our advanced liveness detection will identify the attempt and block the fan identity verification process. This ensures that the person taking the selfie is genuine and prevents fraud.

TruCrowd technology will detect the expired document and deny further progress in the fan identity verification process, requiring the individual to present a valid document.

TruCrowd can detect fraudulent attempts by analyzing various elements of the document, including the MRZ zone, NFC chip, fonts, and field placement. The system can also identify if the original face image has been obscured by an alternative.

To facilitate entry to games or concerts using facial recognition, we require the storage of a biometric template of your face. When you approach the face ticket gate at the stadium, the camera identifies your face from the image, extracts your biometric template, and compares it to the watchlist of approved faces eligible to enter the specific game through that gate.

The technology utilized here is known as biometric identification, specifically 1:N comparison, where the biometric template extracted from the camera at the gate is compared with the N face templates stored in the database.

Yes, TruCrowd Face Ticket Technology is designed for compatibility with existing venue management systems and turnstiles. 

It offers flexible implementation, either by integrating with your current gates equipped with cameras or through our advanced electronic gates, enhancing access control through seamless communication with our Identity Engine via REST API.

Integrating TruCrowd with your existing ticketing system is straightforward, thanks to our Digital Onboarding Toolkit’s user-friendly APIs. These APIs are well-documented and designed for seamless integration into web, Android, and iOS platforms. This allows for easy incorporation of selfie and ID capture features into your ticketing sales website or application, facilitating efficient remote identity verification.

Face recognition and face verification differ in their application and user interaction. In the case of a face ticket, face verification occurs during the onboarding process. It’s used to confirm your identity against an official government-issued ID. This process mirrors the traditional method used by authorities in person. For example, a police officer or clerk compares the photo on your ID with your physical appearance to verify your identity. Biometric facial recognition algorithms perform the same action, but more accurately and quickly. The computer analyzes the photo on your ID and compares it to a selfie you provide. This is technically known as 1:1 verification, where one photo from your ID is matched against one photo from your selfie. This clever method allows access to various services that were previously restricted to in-person interactions, all from the comfort of your home or while on the go.

On the other hand, face identification involves comparing your face against many others in a database, similar to how a person searches for someone in a crowd. This operation is technically referred to as 1:N identification, where one face from the camera capture is compared against numerous faces in the database. Face identification is used at stadium gates when entering with a face ticket. The access control system contains a watchlist of faces permitted to enter the venue because their face is associated with a ticket. Since this operation requires significant computing power, actual photos of faces aren’t compared. Instead, smart algorithms translate facial features into a unique string of characters called a biometric template—a hash or code specific to each face. During the face scan at the gate, your biometric template is extracted and verified against the list of templates in the database.