AI + Security · Public GitHub Repository

Privacy-Safe Multimodal Authentication

Face, Voice & Speech Verification

A privacy-aware authentication workflow integrating face verification, voice verification, speech recognition, multimodal fusion, and FastAPI backend logic.

RoleMain Model & Integration Contributor

Year2025

StatusPublic GitHub Repository

Privacy-Safe Multimodal Authentication project cover

Conceptual project cover

OVERVIEW

One authentication workflow, multiple signals.

Privacy-Safe Multimodal Authentication is an applied AI security project that combines face verification, speaker verification, and optional offline speech-to-text within a modular end-to-end workflow. The system demonstrates how multiple identity signals can be coordinated through fusion logic and surfaced through a practical application interface rather than evaluated only as isolated models.

CORE CAPABILITIES

Face, voice, optional speech, and fusion.

Face Verification

Face embeddings are used to support identity verification from visual input.

Voice Verification

Speaker embeddings provide a second biometric signal for identity matching.

Optional Offline STT

Vosk can capture or confirm a spoken name locally without requiring cloud speech recognition.

Fusion Logic

Outputs from the available modalities are coordinated into a final verification workflow.

SYSTEM WORKFLOW

From multimodal input to a verification decision.

The project is structured as an application pipeline rather than a single model demo.

01CaptureFace · Voice · Optional spoken name

02RepresentFace and speaker embeddings

03VerifyModality-specific matching

04FuseCoordinate available signals

05HandleUI · analytics · approval workflow

PRIVACY-SAFE PUBLISHING

Responsible sharing is part of the engineering.

Biometric projects can expose sensitive identity data if they are published carelessly. The public repository therefore retains runnable structure and documentation while removing or replacing private images, recordings, identities, and attempt logs.

No real biometric dataset shipped publicly

Database structures are represented through safe templates/placeholders

Private attempt logs and identifying media are not exposed

APPLICATION & ANALYTICS

System views from the implemented prototype.

The screenshots below are sanitized for public portfolio use and demonstrate the dashboard, evaluation, and administrative workflow without exposing identifying media.

Authentication system dashboard with identifying media redacted — **Main Dashboard**System overview with identifying content removed for public display.

Authentication system ROC analytics view — **Analytics / ROC View**Evaluation view for recognition-mode performance analysis.

Authentication system confusion matrix and evaluation metrics — **Confusion Matrix & Metrics**Performance summary and fold-level evaluation visualization.

Authentication system pending approvals administration view with sensitive fields redacted — **Administrative Review**Pending-approval workflow with private fields hidden for public presentation.

ENGINEERING FOCUS

More than biometric inference.

The project connects model components with backend logic, storage templates, analytics, user interaction, and administrative review. That system-level integration is a central part of the work.

Modular pipelineSeparate face, voice, fusion, client, and server components.

Offline optionOptional Vosk-based speech recognition can run locally.

Application workflowDashboard, analytics, enrollment, and approval interfaces support the technical pipeline.

Privacy-aware releaseThe public repository is structured to remain understandable without distributing real private data.

TECH STACK

FastAPIPyTorchOpenCVInsightFaceSpeechBrainVosk

FaceInsightFace · OpenCV

VoiceSpeechBrain · PyTorch · torchaudio

SpeechVosk (optional offline STT)

BackendFastAPI · Uvicorn

SystemFusion logic · database templates · logging structure · admin workflow

PUBLIC REPOSITORY

Explore the privacy-safe project structure on GitHub.

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