The COVID-19 pandemic provided a practical approach to practice digital solutions. Although the pandemic is over, many companies are still moving toward digital solutions regarding business development. Such businesses utilize online boarding services to reach a large number of clients all over the globe. However, digitization has brought a kind of revolution in the business industry but it provided confidence to imposters regarding cybercrimes. 

Why Is It Essential For Companies To Detect Spoofing?

Companies must utilize spoof detection services to prevent unauthorized clients from being onboard.   Biometric face recognition facilitates companies to identify an authentic client and make their system secure. Many criminals use fake images and videos to pose as real ones, which may lead companies toward various future complexities.

How Does Biometric Face Recognition Help Firms To Identify Spoofing?

Biometric face scanners enable firms to have liveness detection, which empowers companies to determine whether the image provided is of a real person or is a spoofing attempt. Many criminals use static images and fake videos to be considered real. Biometric face recognition services rely on the uniqueness of human face prints and identify their authenticity. Advanced artificial intelligence (AI) and machine learning (ML) technology integrated into face scanners can differentiate between fake and real interactions. Biometric facial recognition technology is integrated with AI and ML algorithms that analyze facial expressions to ensure that the image or video is real and not fake. 

How Does the Facial Recognition Process Work?

Facial recognition is the very basic step in biometric face verification. It enables firms to legitimate the identity of clients while having them onboard. Biometric face recognition process in the following three steps.

  • Face Detection 

For biometric facial recognition, individuals are asked to provide facial data in the form of images or videos. Moreover, real-time face verification is also done. Therefore,  there are the following two kinds of verification that can be done.

  • Real-Time Face Verification 

Individuals are asked to face the camera, they just have to present their face. It can be done in both online and onsite environments. Customers use webcams for online face recognition or they can provide real-time face images with the help of Androids. These images are detected with the help of face scanners 

  • Images And Videos Face Verification

Biometric face verification may involve a pre-captured image or vi9deo for facial verification. Many clients use selfies and photos for their identification. Imposters try to use fake images to reach companies onboard. 

  • Analysis 

After successful image detection, AI and ML algorithms work to analyze face expressions and facial geometry.  They identify the patterns and follow the conversion to convert them into digital face prints. 

  • Conversion

Algorithms analyze facial geometry that includes various facial nodes such as skin tone, color of eyes, iris depth, face contour, and distance between chin and forehead. These facial nodes are executed and converted into mathematical formulas to be stored in electronic databases in the form of digital face prints.

  • Storage 

After the successful conversion of facial data into digital prints, it is stored in electronic databases. These databases are further used for various cross-matches to identify the authenticity of clients. Digital databases are accessible to verify clients’ identity over different records.

  • Verification

Once the image is captured, it is detected and facial features are analyzed. After analysis, facial data is converted into face prints which are stored in digital databases. Various cross-matches are executed to validate the captured facial prints to identify users’ authenticity. 

Key Takeaways

Biometric face recognition technology provides security to fight against identity theft and face spoofing. It enables firms to mitigate the risk of fraudster. Machine learning facial recognition is employed with AI and ML pre-trained models which provide a robust mechanism to unveil spoofing to reach fraudsters. Additionally, it enables firms to identify clients’ identities and verify their authenticity. It is highly effective for both clients and firms as users get verified online and firms have automated solutions. Additionally, it reduces revenue as it reduces hiring regarding verification processes and provides an error-free identity verification solution.