Since 2021, over $1 billion has been reported lost to crypto scams, affecting more than 46,000 people. These losses are often facilitated through social media platforms, with nearly half of the scams originating from ads, posts, or messages on sites like Instagram and Facebook. Investment scams and imposter scams are standard methods used by fraudsters.
Therefore, these digital apps require advanced solutions, such as face recognition online, to identify users while logging into their accounts. This technology can also prevent identity theft and other potential risks of financial losses.
This article will explore how face recognition technology dissuades fraud and works for online mobile apps and
Key Insights of the Article
- What is facial recognition?
- How does the facial recognition system work?
- The advantage of face recognition
- AI Facial Recognition incorporated with ML
What is Facial Recognition?
Face recognition technology was developed in the 1950s but has evolved to technological advancements and is now incorporated in new mechanisms to detect countenances. It is a type of computer vision that uses optical input to analyze an image. In this case, it looks mainly at faces that appear in the image. Facial recognition technology can be used as a building block to support other capabilities like face identification, grouping, and verification.
This process is optimal for detecting the pseudo faces, and those that are not relevant to the data. It will prevent the fraudsters from accessing the user accounts and only allow the authentic holders to access their digital financial spaces. It deters identity fraud and other economic losses by creating an alarming situation at the backend of the FinTech and Crtpro apps.
How Does Facial Recognition System Work?
Facial recognition online works more superficially and gives accurate and authentic results within seconds. This process works in the following manner:
- Image Capture: The system captures an image of a person’s face or a pre-captured one is submitted.
- Face Detection: The multifaceted algorithm detects and isolates the face in the image by using machine learning
- Facial Feature Extraction: The system then extracts key facial features like eyes, nose, and mouth.
- Face Alignment: These features are normalized and aligned for consistency and to get the optimal results from the system..
- Feature Encoding: The face now is then converted into a unique numerical code or a biometric template.
- Database Comparison: This code or the template is compared against an available database of known faces.
- Matching and Identification: If a match is found in the database, then the person is identified and if there is no correspondence, then he/she is not verified by the system.
- Decision Making: Based on the match, the access is granted or denied.
- Continuous Learning: The systems improve over time with new data and feedback by learning continuously.
AI Facial Recognition With Machine Learning
Artificial intelligence (AI) and machine learning (ML) are incorporated in facial recognition to optimize the efficiency of the process. This boosts the numerical coding and does it with the prefound algorithms. Facial recognition is combined with other identifying factors to verify a person’s identity so they can securely access their digital accounts. This technology also uses the convolutional neural network (CNN), which has the ability to diagnose suspicious faces from the criminal database for prevalent zones.
Benefits of Face Recognition in Digital Apps
Face recognition technology is ever-evolving and has changed the dynamics of identity verification. Here are the benefits of face recognition online in digital apps presented in bullet points:
- It provides robust authentication and authorization that reduces unauthorized access to the user.
- It enables quick and easy access to apps without passwords or PINs, as the user just needs to identify their face.
- It customizes user experience based on recognized preferences and behavior by dropping cookies and noticing their activities.
- It helps detect and prevent identity theft and fraud attempts if ever made by fraudsters.
- It speeds up processes of identity verification and access control quickly.
- It offers insights into user demographics and behavior for targeted marketing and services.
- It seamlessly integrates with other technologies for enhanced functionality and security.
- It enhances user satisfaction by offering a modern and secure login method.
Final Statment
Incorporating face recognition technology into crypto and fintech apps offers a robust solution to combat identity fraud and enhance financial security. By utilizing facial recognition, these platforms can ensure the access of only legitimate users, thereby reducing the risk of unauthorized access and financial loss. Integration of AI and machine learning further optimizes the process, providing real-time protection and streamlining user experiences, ultimately safeguarding the financial integrity of the industries.
