Offline Face Detection & Comparison for the Insurance domain using FaceNet | Nitor Infotech
Send me Nitor Infotech's Monthly Blog Newsletter!
×
nitor logo
  • Company
    • About
    • Leadership
    • Partnership
  • Resource Hub
  • Blog
  • Contact
nitor logo
Add more content here...
Artificial intelligence Big Data Blockchain and IoT
Business Intelligence Careers Cloud and DevOps
Digital Transformation Healthcare IT Manufacturing
Mobility Product Modernization Software Engineering
Thought Leadership
Aastha Sinha Abhijeet Shah Abhishek Suranglikar
Abhishek Tanwade Abhishek Tiwari Ajinkya Pathak
Amit Pawade Amol Jadhav Ankita Kulkarni
Antara Datta Anup Manekar Ashish Baldota
Chandra Gosetty Chandrakiran Parkar Deep Shikha Bhat
Dr. Girish Shinde Gaurav Mishra Gaurav Rathod
Gautam Patil Harish Singh Chauhan Harshali Chandgadkar
Kapil Joshi Madhavi Pawar Marappa Reddy
Milan Pansuriya Minal Doiphode Mohit Agarwal
Mohit Borse Nalini Vijayraghavan Neha Garg
Nikhil Kulkarni Omkar Ingawale Omkar Kulkarni
Pooja Dhule Pranit Gangurde Prashant Kamble
Prashant Kankokar Priya Patole Rahul Ganorkar
Ramireddy Manohar Ravi Agrawal Robin Pandita
Rohan Chavan Rohini Wwagh Sachin Saini
Sadhana Sharma Sambid Pradhan Sandeep Mali
Sanjeev Fadnavis Saurabh Pimpalkar Sayanti Shrivastava
Shardul Gurjar Shravani Dhavale Shreyash Bhoyar
Shubham Kamble Shubham Muneshwar Shubham Navale
Shweta Chinchore Sidhant Naveria Souvik Adhikary
Sreenivasulu Reddy Sujay Hamane Tejbahadur Singh
Tushar Sangore Vasishtha Ingale Veena Metri
Vidisha Chirmulay Yogesh Kulkarni
Artificial intelligence | 05 Jun 2019 |   6 min

Offline Face Detection & Comparison for the Insurance domain using FaceNet

featured image

The insurance industry is estimated as most competitive but a less predictable domain. Insurance policies safeguard against uncertainties and hence are more prone to risks. Therefore, it has always been dependent on statistics. Certainly, the insurance companies have been benefited from data science applications.

One of the most interesting use case we came across is face detection in images and comparison of two or more images to predict whether faces identified in both images are of the same persona.

The detailed requirement is that an insurance executive should be able to capture both images from his/her mobile device. The device will then process those images and compare personas in images to provide outcome.

Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently presents challenges to current approaches. Hence, we choose to use the FaceNet system to measure face similarity. Firstly, we have used CNN to identify faces. Secondly, we have used FaceNet to get vector mapping of images and then compare those vector to predict similarity.

What is Facenet?

FaceNet is a system that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity. Once this space has been produced, tasks such as face recognition, verification, and clustering can be easily implemented using standard techniques with FaceNet embeddings as feature vectors.

FaceNet maps a face into a 128D Euclidean space. The L2 distance (or Euclidean norm) between two face embeddings corresponds to its similarity. This is exactly like measuring the distance between two points in a line to know if they are close to each other.

The FaceNet model is a deep convolutional network that employs triplet loss function. Triplet loss function minimizes the distance between a positive and an anchor while maximizing the distance between the anchor and a negative.

Why FaceNet?

FaceNet performs really well on real images. It builds on the Inception ResNet v1 architecture and is trained on the CASIA-WebFace and VGGFace2 datasets. FaceNet’s weights are optimized using the triplet loss function, so that it learns to embed facial images into a 128-dimensional sphere.

A Complete Solution

This is how we ensured a flawless implementation of FaceNet:

  • Accepted two images from user and resized.
  • Detected faces from both the images using Multi-task Cascaded Convolutional Networks (MTCNN).
  • Cropped the faces from the images.
  • Two cropped images were provided as input to face net model, which outputs two vectors of 512 dimensions.
  • Used Tensorflow implementation of MTCNN and FaceNet in android application.
  • Applied Euclidean distance formula to calculated distance between two vectors.
  • Compared distance with predefined threshold value, which can be set as per the analysis result while testing phase.
  • Completed face verification using the result of that comparison.

Screenshots of matches  Screenshot of no matches 

 Conclusion:

Face technology is making its way into the mainstream. Furthermore, there is a growing list of companies offering developer friendly face API through their SaaS platforms, making it a lot easier and cheaper to incorporate state-of-the-art face technology into different types of products.

By now, you should be familiar with how face recognition systems work and how to make your own simplified face recognition system using a version of the FaceNet network.

Related Topics

Artificial intelligence

Big Data

Blockchain and IoT

Business Intelligence

Careers

Cloud and DevOps

Digital Transformation

Healthcare IT

Manufacturing

Mobility

Product Modernization

Software Engineering

Thought Leadership

<< Previous Blog fav Next Blog >>
author image

Nitor Infotech Blog

Nitor Infotech is a leading software product development firm serving ISVs and enterprise customers globally.

   

You may also like

featured image

10 Heuristic Principles in UX Engineering

Say, you’ve built a modern, cutting-edge application. It has a complex, multi-layered user interface (UI), that is the basis for some amazing features. Since you’re the one who has built the applic...
Read Blog


featured image

ETL Testing: A Detailed Guide

Just in case the term is new to you, ETL is defined from data warehousing and stands for Extract-Transform-Load. It covers the process of how the data is loaded from the multiple source system to t...
Read Blog


featured image

Getting Started with ArcGIS Online

GeoServer is an open-source server that facilitates the sharing, processing and editing of geospatial data. When we are dealing with a large set of geospatial d...
Read Blog


subscribe

Subscribe to our fortnightly newsletter!

We'll keep you in the loop with everything that's trending in the tech world.

Services

    Modern Software Engineering


  • Idea to MVP
  • Quality Engineering
  • Product Engineering
  • Product Modernization
  • Reliability Engineering
  • Product Maintenance

    Enterprise Solution Engineering


  • Idea to MVP
  • Strategy & Consulting
  • Enterprise Architecture & Digital Platforms
  • Solution Engineering
  • Enterprise Cognition Engineering

    Digital Experience Engineering


  • UX Engineering
  • Content Engineering
  • Peer Product Management
  • RaaS
  • Mobility Engineering

    Technology Engineering


  • Cloud Engineering
  • Cognitive Engineering
  • Blockchain Engineering
  • Data Engineering
  • IoT Engineering

    Industries


  • Healthcare
  • Retail
  • Manufacturing
  • BFSI
  • Supply Chain

    Company


  • About
  • Leadership
  • Partnership
  • Contact Us

    Resource Hub


  • White papers
  • Brochures
  • Case studies
  • Datasheet

    Explore More


  • Blog
  • Career
  • Events
  • Press Releases
  • QnA

About


With more than 16 years of experience in handling multiple technology projects across industries, Nitor Infotech has gained strong expertise in areas of technology consulting, solutioning, and product engineering. With a team of 700+ technology experts, we help leading ISVs and Enterprises with modern-day products and top-notch services through our tech-driven approach. Digitization being our key strategy, we digitally assess their operational capabilities in order to achieve our customer's end- goals.

Get in Touch


  • +1 (224) 265-7110
  • marketing@nitorinfotech.com

We are Social 24/7


© 2023 Nitor Infotech All rights reserved

  • Terms of Usage
  • Privacy Policy
  • Cookie Policy
We use cookies to ensure that we give you the best experience on our website. If you continue to use this site we will assume that you are happy with it. Accept Cookie policy