17972329. HIGH ACCURACY PEOPLE IDENTIFICATION OVER TIME BY LEVERAGING RE-IDENTIFICATION simplified abstract (Meta Platforms Technologies, LLC)

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HIGH ACCURACY PEOPLE IDENTIFICATION OVER TIME BY LEVERAGING RE-IDENTIFICATION

Organization Name

Meta Platforms Technologies, LLC

Inventor(s)

Mahdi Salmani Rahimi of San Francisco CA (US)

Rahul Nallamothu of Redwood City CA (US)

Samuel Franklin Pepose of Palo Alto CA (US)

HIGH ACCURACY PEOPLE IDENTIFICATION OVER TIME BY LEVERAGING RE-IDENTIFICATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 17972329 titled 'HIGH ACCURACY PEOPLE IDENTIFICATION OVER TIME BY LEVERAGING RE-IDENTIFICATION

The abstract describes a method involving the use of machine learning models to authenticate participants in a group based on appearance information captured by a camera.

  • The method determines the location of participants in an environment and identifies specific body regions of each participant.
  • Appearance information of these body regions is detected and used to calculate a confidence score for matching with pre-registered participant profiles.
  • The confidence score is updated based on additional appearance information from subsequent frames.
  • If the updated confidence score surpasses a predetermined threshold, the participant is authenticated.

Potential Applications: - Security systems for access control - Attendance tracking in educational settings - Identity verification in virtual meetings

Problems Solved: - Ensures accurate participant authentication - Reduces the risk of unauthorized access - Streamlines identification processes in group settings

Benefits: - Improved security and access control - Efficient and reliable participant authentication - Enhanced user experience in virtual environments

Commercial Applications: Title: "Enhanced Participant Authentication System for Secure Environments" This technology can be applied in industries such as: - Banking and finance - Healthcare - Event management

Questions about Participant Authentication: 1. How does this method improve upon traditional authentication processes? 

  - This method enhances security by using appearance information for participant authentication, reducing the risk of unauthorized access.

2. What are the potential limitations of using machine learning models for participant authentication? 

  - Machine learning models may require continuous training and updates to adapt to new appearance information and profiles.


Original Abstract Submitted

In one embodiment, a method, by one or more computing systems, includes determining, based on frames captured by a camera, a plurality of participants are located in an environment, locating, within a first frame, a first body region of a first participant of the plurality of participants, detecting, at a first time, appearance information of the first body region of the first participant, calculating, using one or more machine-learning models, a confidence score corresponding to a match between the appearance information of the first participant at the first time and one or more profiles of pre-registered participants, updating, using the one or more machine-learning models, the confidence score based on one or more additional appearance information detected within additional frames, determining whether the updated confidence score is above a predetermined threshold, and in response to determining the updated confidence score is above the predetermined threshold, authenticating the first participant.

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