FaceOff Lite

Real-Time Edge & On-Device Behavioral Biometric Authentication

This version is optimized for real-time, low-latency applications like identity verification, liveness checks, and continuous authentication, running directly on edge devices or integrated with live camera feeds.

 

• Core Principle:

Behavioral Biometrics for Liveness & Intent. Faceoff Lite focuses on real-time authentication by analyzing a combination of facial, typing, and other behavioral biometric patterns.

• Technical Implementation & Features:

 Facial Feature Extraction: Utilizes a self-supervised learning technique, to generate robust 512-D face embeddings. It avoids overfitting and performs better in diverse lighting, crucial for uncontrolled environments.

 Physiological Liveness (Heart Rate & SpO2): Uses contactless rPPG to confirm the user is a live human with genuine physiological signals, defeating simple spoofing attacks.

 Facial and Eye Activities: Performs full facial scanning through activities and eye tracking through perform random activities as per the instructions appearing on the screen.

 Physiological Liveness (Heart Rate & SpO2): Uses contactless scanning of heart rate and SpO2 to confirm the user is a live human with genuine physiological signals, defeating simple spoofing attacks.

 Keystroke Dynamics Tracking: Employs a lightweight deep learning model. It enables real-time analysis of a user's unique typing rhythm and style when they enter a PIN or password.

 When a bank official asks questions during a video KYC session, Faceoff Lite's emotion analysis modules (Facial, Eye, Heart rate SPO2) run in real-time.

 This provides the official with insights into the applicant's stress, confidence, or potential coercion, adding a crucial layer of security beyond a simple face match.

 Faceoff Lite is designed with optimized SDKs and APIs to integrate directly with live video streams from webcams or existing CCTV infrastructure, enabling its use for continuous authentication or monitoring in various settings.

• Certification Benefits of This Architecture:
  • o Quality of AI Analysis: The use of advanced models provides measurably superior performance in challenging real-world conditions (diverse lighting, noise). This can be verified through benchmark testing.
  • o Real-Time Performance: The use of lightweight models ensures that processing can happen at the edge or on-device with minimal latency, a key requirement for real-time authentication.
  • o Advanced Threat Mitigation: The combination of facial, and behavioral biometrics provides a multi-layered defense that is significantly harder to spoof than any single biometric factor.