AI & Machine Learning
Private

Biometric Authentication & ML.NET Client

C#Windows FormsML.NETOpenCvSharpFirebaseResNet50Haar Cascades

A secure desktop client integrating real-time OpenCV facial bounding, ML.NET deep learning inference, and Firebase for biometric two-factor authentication.

Tech Stack

C#Windows FormsML.NETOpenCvSharpFirebaseResNet50Haar Cascades

System Metrics

▹Strict memory lifecycle management of unmanaged OpenCV `Mat` and `VideoCapture` objects via explicit `Dispose` blocks to prevent memory leaks during continuous webcam streaming
▹Low-latency desktop inference achieved by statically compiling the ML.NET prediction engine directly into the application binary

Why Did I Build This?

"Developed during an internship to solve the vulnerabilities inherent in traditional static-credential systems. I needed a dynamic pipeline that validates both cryptographic password hashes and real-time spatial biometrics before granting system access. To train the underlying neural network, I utilized datasets autonomously extracted via my proprietary C++ `OpenCV-Face-Features-Toolkit-For-ML` utility."

Architecture & Decisions

The application is engineered on .NET 6.0. Real-time video capture and spatial face bounding are offloaded to `OpenCvSharp` leveraging Haar Cascades to minimize CPU overhead during the detection phase. Isolated facial matrices are passed into an embedded ML.NET computational graph—a pre-trained ResNet50 Deep Neural Network—for hardware-accelerated classification. State synchronization and user registry validation are managed asynchronously via Firebase Realtime Database (`FireSharp`), enforcing SHA-256 cryptographic hashing for credential storage.

Key Features

  • 01.Real-time video capture and spatial facial isolation utilizing `OpenCvSharp` native bindings
  • 02.Embedded ML.NET inference pipeline utilizing a ResNet50 DNN architecture for high-accuracy classification
  • 03.Asynchronous Firebase Realtime Database integration for distributed user registry management
  • 04.Biometric Two-Factor Authentication (2FA) combining SHA-256 credential hashing with localized neural network validation