Technological advancement has shown miracles in every industry, and the benefits of digital media can not be denied. Face recognition deep learning is one of them, as it is an additional security layer for the biometric checks. These solutions are completely reliable as advanced machine learning tools are integrated. In Indonesia 2022, 976 million cyber attacks were recorded; this considerable number of cases is alarming for the country. Therefore, legal authorities have made it essential for organizations to comply with Anti Money Laundering (AML) regulations. Compliance with these rules is possible by integrating biometric checks in the company.
What is Face Liveness Verification?
Facial recognition verification is the method of user authentication, and this process is done to ensure that the client is live; this step mitigates fraudulent activities, as only verified individuals will affiliate with the business. Companies can even verify their business partners through it, as they can get better knowledge about the organizations’ products, services, and strategies. The chances of getting scammed are lowered when the client’s information is known correctly.
Functioning of Biometric Face Recognition
The following steps are involved in the working of the face recognition solution:
- Face Detection
The first step involves detecting the face of the client, and the scanner has a built-in feature that detects that the live person is present in front of the scanner. It can even be used to search for a person in the crowd. Social media apps like Facebook, Snapchat, and Instagram have this feature; users can apply filters to their pictures and videos.
- Face Alignment
The face recognition solutions make a template of the facial features, which is readable. The distance between the eyes, nose, and lips is measured.
- Face Extraction
Face recognition services extract the features from the face and then match them with the previously stored templates. Only required marks are removed and are then compared for the authentication process.
- Face Verification
The last step involves verifying the user; if both the templates match, the client is authentic. Otherwise, authentication is rejected. A reg flag is shown for the customers whose verification is denied. Sometimes, such customers are asked to submit further documents to check their authenticity.
Importance of Face Recognition Deep Learning
Deep learning is performed to check the liveness of the customer; hackers present a fake image or video of the client to bypass the account. They use the 3D silicon masks to dodge the camera, but deep learning has resolved this issue, as it is an advanced form of biometrics. Face recognition deep learning does not allow hackers to decode the algorithm, as it checks the texture, depth, and color of the client’s skin. The fake masks do not contain proper depth, so it is easy to detect them using advanced biometric check features.
Uses of Face Liveness Verification
The following are the top uses of face recognition deep learning
- Unlocking the phone
These solutions are installed in mobile phones to unlock them; the iPhone has first used this feature. Users do not have to memorize the password; the phone is unlocked by pointing fingers or facing the camera.
- Finding Target Client
The face recognition deep learning database contains a complete record of the customers; for research purposes, companies need the user’s information. The organizations can use the users’ data from the biometric system for marketing. In this way, the companies’ expenditure is reduced, so they do not have to spend money on finding the target clients.
- Finding Missing Persons
The best feature of this solution is that missing persons can be searched by it, and the picture of that individual is uploaded on the scanner. The respective authorities are immediately informed Whenever the desired face comes before the camera.
- Check-in and Check-out Record
Companies can keep a record of the activities of their employees, and they do not have to monitor them continuously; the scanner keeps the description of the operations of the staff. Just as organizations do not have to put in writing the attendance, face recognition deep learning notes the presence of the operators.
Conclusion
Organizations can deeply verify the face of their clients and allow only authentic clients to become part of the business. These solutions provide seamless, user-friendly, and smooth services to their users and simplify the company’s daily activities. Stakeholders prefer the company that keeps their information safe; the company must keep the users’ credentials safe. Face recognition deep learning preserves the data of both the client and the organization. The client retention rate is enhanced by integrating these solutions, as they facilitate the users to their maximum.
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