Facial Recognition App for Healthcare Industry

Face Learning

FaceLearning is a dynamic facial recognition app powered by artificial intelligence, machine learning and deep neural algorithms. Designed to harness natural language processing technology, the app is used by a high-end global healthcare service provider.

The app helps the company in enforcing biometric control over its information assets for enforcing international-grade security standards. The app allowed users to submit their critical healthcare data securely without getting worried about the misuse of data.

Challenges

The client operated in a challenging ecosystem governed by several regulations concerning end-user data management. It was the responsibility of the company to enforce high-end security standards while asking patients/end-users for their personal health information (PHI). Failing to mitigate risks and protect data can make the company liable for fines and litigations.

The biggest challenge was to create an integrated solution that does not make the information submission and access process complex. At the same time, the information needed to be accessible to only those who are entitled to access the information.

Our Team Tackled the Challenges by Performing the Following Tasks

  • Ideating the Integration of Facial Recognition Technology for Enforcing Biometric Security Standards
  • Designing Simplified UI/UX for Facial Recognition App
  • Developing the Entire Backend by Leveraging Latest Technologies
  • Creating a Robust Database Management System for Information Sharing
  • Testing & Deploying the App for Streamlining Routine Operations without Disruption

Solution

Team Artecha analyzed the requirement in-depth and created a multi-pronged strategy to address security challenges. Making use of progressive technologies like machine learning, artificial intelligence and deep neural algorithms, Team Artecha designed and developed the facial recognition mobile app.

Artecha made use of advanced frameworks and programming languages like Python, PHP, Java & HTML to create a powerful solution. Flutter framework was used to create a cross-platform application. The facial recognition app was designed keeping the workflows in the healthcare industry in mind.

Stripe API was integrated into the mobile app to streamline online payments and integrate payment management.

The app made use of natural language processing and deep neural networks to authenticate the information requests, detect anomalies and/or process the end-user requests in real time. Using an advanced algorithm, the app validates user requests and also facilitates information exchange with end-to-end encryption using facial recognition technology.

On top of everything, Team Artecha created a long-term implementation and deployment strategy that boosted user engagement, improved security standards and made the company more efficient in managing personal healthcare information.

Facial Recognition App Workflow

  • Mobile app triggered via a dedicated mobile app installed in the user’s mobile
  • Mobile camera detects a user’s face using deep neural networks, artificial intelligence, machine learning and computer vision technology
  • Captured data is sent back to the healthcare company’s database on AWS cloud to verify against available information
  • Matches are detected by analyzing available faces in the database automatically
  • The user gets to access available information and/or submit new health information as required

All this happens in real-time, automatically without any manual intervention

Technology Stack

  • Frameworks & Languages: Python | PHP | Java | Flutter | HTML
  • Database: Cloud | AWS
  • API: Stripe

Key Benefits & Results

The FaceLearning app was embedded in routine operations which helped the company in improving its security levels. Users were more confident to submit their information to the company and at the same time, facial recognition helped the company in securing critical healthcare data.

The app helped the company in mitigating cybersecurity risks, while bringing down the cost of operational security enforcement. On another note, users became more engaged with the company owing to increased confidence in company’s security measures.

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