FindFace Recognition


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FindFace – New Generation Face Recognition

Face Recognition Revolution: Faces to track; Known criminals and suspects; Investigations; Missing children and elders; Crowds management and analytics.
Existing Technologies: Not accurate enough: too many false positives; Too slow: blacklist size is limited; Require expensive hardware.
FindFace New Generation Neural Network: Very accurate – even more accurate than a human; Very fast – searches among billion photos in less than half second.

FindFace for security

World’s Best Accuracy: 95% rank-10 probability among 10K photos; 88% rank-10 probability among 1M photos; 99% verification accuracy.

World’s Best Performance: Half-second search time for 1 billion photos; Unique index performs very fast search while maintaining the highest accuracy;The index needs just 20 GB for 500 million photos.

MegaFace Challenge Winner: First place among 90+ competitors, beating even Google; First benchmark to evaluate large-scale face recognition solutions;
Largest real-world photo set with different poses, lighting, obstructions, etc.

Proven by A web app where everyone can upload a photo to find a person’s profile on the network; 250 million photos of 100 million
people; 50 searches per second on five Amazon servers.

Security Use

  • Mobile software for police and transport police officers
  • Fixed surveillance cameras integrated into the urban landscape
  • Cameras in transport infrastructure facilities (subway, airports, railway stations)
  •  Cameras for government establishments, police departments, sensitive sites, etc
  •  Face detection and identification in a video stream: Including surveillance CCTV cameras in public spaces
  • Restricted site access and database search: E.g. for fans blacklisted from a stadium
  • Personal identity verification: Including entrance facilities and mobile police officers
  • Monitoring movement of specific people or crowds at infrastructure facilities and in the city

FindFace for retail

High accuracy

Identification – Database 10.000 – Accuracy 93%
Verification – Comparison 1:1 – Accuracy more than 99%

You don’t need to upgrade hardware
High recognition accuracy even on low-quality images and resolutions (from street and panoramic CCTV cameras, low-resolution webcams, etc.);
It is possible to integrate with the installed systems of photo and video recording.

Performance and scalability
Database Search:
2 million photos – 0,2 sec / 250 million photos – 0,3 sec / 1 billion photos – 0,5 sec
Feature vector size is less than 1 KB. 50 searches per second on five Amazon servers for 250 million photos dataset.

Sustainability to appearance changes
Face Vegetation / Glasses / Getting old / Occlusions / Head turns / Emotions

Gender, age and emotions recognition
Emotions – Recognizing of the primary and the secondary emotion among 7 basic and 50 side emotions. The EmotioNet Challenge 2017 Winner.
Age – Recognizing of the age within five years with 95% accuracy.
Gender – Recognizing of the gender with 95% accuracy.

Marketing and analytics

  • General client flow to store
  • Detection of specific client group
  • Obtaining data about the specific client as only he enters the store
  • Using the client data by a shop assistant
  • Targeted offline advertisement
  • Heat maps by demographic categories
  • Standart routes by demographic categories

The quality of service

  • Nps calculation
  • The quality of the service at cashier’s desk
  • Loyalty program
  • Analysis of promos

Security and personnel management

  • Be aware of shoplifters
  • Prevention of the alcohol and tobacco sales to minors
  • Employees access control in store areas
  • Worktime control

FindFace – Enterprise Server SDK

The FindFace SDK is a C library that provides access to the cutting-edge face recognition technology based on neural networks. The SDK allows you to
quickly and accurately solve the 3 key tasks of face recognition:

  • Verification: It takes ~ 75 ns to compare 2 biometric samples and estimate the probability of their belonging to the same person.
  • Liveness check: Distinguish a live face in front of a camera from a photo on paper or mobile device screen.
  • Face attributes extraction: Recognize age, gender, emotions, glasses, beard, and other attributes.
  • Face detection: Finds face fragments in an image and returns a bounding box and control points (eyes, nose, corners of the mouth) for each fragment.
  • Biometric sample extraction: 500 ms is the time needed to extract a face biometric sample and save it in a temporary binary format. The sample can
    be later saved to a database and used for face verification.


  • Algorithm accuracy and speed
  • Ability to work online/offline
  • Neural networks


  • High-speed calculations
  • Liveness
  • Multithreading support


  • Friendly C/C++ code
  • Face features recognition
  • GPU

Typical cases

  • E-gate
  • Access control


  • Authentication
  • Wearables