Meta platforms technologies, llc (20240135701). HIGH ACCURACY PEOPLE IDENTIFICATION OVER TIME BY LEVERAGING RE-IDENTIFICATION simplified abstract

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 20240135701 titled 'HIGH ACCURACY PEOPLE IDENTIFICATION OVER TIME BY LEVERAGING RE-IDENTIFICATION

Simplified Explanation

The abstract describes a method for authenticating participants in an environment using camera frames and machine learning models.

• Determining the location of multiple participants in an environment based on camera frames
• Locating a specific body region of a participant in a frame
• Detecting appearance information of the body region at a specific time
• Calculating a confidence score for matching the appearance information with pre-registered profiles
• Updating the confidence score with additional appearance information from more frames
• Authenticating the participant if the confidence score exceeds a threshold

Potential Applications

This technology could be applied in security systems, access control, virtual meetings, and event management.

Problems Solved

This technology solves the problem of accurately identifying and authenticating participants in a crowded environment using visual information.

Benefits

The benefits of this technology include improved security, streamlined access control processes, and enhanced participant management in various settings.

Potential Commercial Applications

The potential commercial applications of this technology include security systems for buildings, event management software, virtual meeting platforms, and access control solutions.

Possible Prior Art

Prior art in this field may include facial recognition systems, biometric authentication methods, and object tracking technologies.

How does this technology handle privacy concerns related to capturing and analyzing participant appearance information?

This technology may raise concerns about privacy and data protection. It is essential to address these issues by implementing strict data security measures, obtaining consent from participants, and complying with relevant regulations.

What are the limitations of this technology in terms of accuracy and scalability?

The accuracy of participant authentication may be affected by factors such as lighting conditions, occlusions, and variations in appearance. Scalability could be a challenge when dealing with a large number of participants or complex environments. Ongoing research and development are needed to address these limitations and improve the performance of the technology.

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.