Overview
VeriFire is a secure virtual interview platform designed to authenticate candidates and prevent cheating during online interview sessions. It enables recruiters to conduct safe and verified meetings using advanced identity verification, behavior tracking, and fraud detection technologies. VeriFire ensures a trustworthy remote hiring process with multi-factor authentication, face recognition, candidate behavior analysis, and secure communication channels.
Problem & Goal
Problem:
Online interviews have become widely used, but candidates can easily cheat using external help, hidden devices, or impersonation. Recruiters face difficulties verifying authenticity, detecting fraudulent behaviors, and maintaining interview integrity. This leads to poor hiring decisions, reduced trust in remote recruitment, and lost opportunities for genuinely skilled local talent.
Goal:
The goal of VeriFire is to create a secure, reliable, and fraud-resistant interview environment that allows recruiters to authenticate candidates, detect suspicious behavior in real-time, and maintain data privacy. It aims to improve global remote hiring by enabling trustworthy, legitimate interview processes.
Objectives
- Provide a secure platform for verified online interviews.
- Ensure candidate authenticity using biometric and multifactor verification.
- Detect suspicious or fraudulent behavior during interviews.
- Protect user data and communication using encrypted channels.
- Allow recruiters to manage access levels and conduct safe meetings.
- Enhance trust and transparency in remote hiring environments.
Scope
- Multifactor authentication before joining meetings.
- Face ID and identity verification using scanning models.
- Behavior tracking such as looking directions, phone usage, and presence of multiple persons.
- Anti-fraud mechanisms to detect abnormal actions or disturbances.
- Encrypted communication tunnel for secure video streaming.
- Access level separation for recruiters vs. candidates.
- Data protection using Clerk authentication API.
Tech Stack
VeriFire is built using Next.js and TypeScript for the frontend and application logic. TensorFlow and Google Teachable Machine are used for real-time behavior tracking and face verification. Authentication and data security are managed through Clerk API, while encrypted channels ensure private communication between the recruiter and candidate.
Screenshots & UI
Key Features
- Multifactor authentication for secure login.
- Face ID identity verification with real-time scanning.
- Anti-fraud measures to detect suspicious actions.
- Behavior tracking such as looking directions, multiple persons, and device usage.
- Data privacy protection with Clerk API.
- Encrypted meeting streams between recruiter and candidate.
- Access level controls to differentiate recruiter and candidate capabilities.