View source for GOOGLE LLC (20240265731). Systems and Methods for On-Device Person Recognition and Provision of Intelligent Alerts simplified abstract

=Systems and Methods for On-Device Person Recognition and Provision of Intelligent Alerts=


==Organization Name==

[[:Category:GOOGLE LLC|GOOGLE LLC]]


[[Category:GOOGLE LLC]]

==Inventor(s)==

[[:Category:Mohammad Afshar of Mountain View CA (US)|Mohammad Afshar of Mountain View CA (US)]][[Category:Mohammad Afshar of Mountain View CA (US)]]

[[:Category:Saajan Shridhar of Mountain View CA (US)|Saajan Shridhar of Mountain View CA (US)]][[Category:Saajan Shridhar of Mountain View CA (US)]]

[[:Category:George Alban Heitz, Iii of Mountain View CA (US)|George Alban Heitz, Iii of Mountain View CA (US)]][[Category:George Alban Heitz, Iii of Mountain View CA (US)]]

[[:Category:Andrew C. Gallagher of Mountain View CA (US)|Andrew C. Gallagher of Mountain View CA (US)]][[Category:Andrew C. Gallagher of Mountain View CA (US)]]

[[:Category:Michael C. Nechyba of Pittsburgh PA (US)|Michael C. Nechyba of Pittsburgh PA (US)]][[Category:Michael C. Nechyba of Pittsburgh PA (US)]]

[[:Category:Joseph E. Roth of Longmont CO (US)|Joseph E. Roth of Longmont CO (US)]][[Category:Joseph E. Roth of Longmont CO (US)]]

==Systems and Methods for On-Device Person Recognition and Provision of Intelligent Alerts - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240265731 titled 'Systems and Methods for On-Device Person Recognition and Provision of Intelligent Alerts

The present document describes systems and methods for on-device person recognition and provision of intelligent alerts. The system includes a decentralized multi-camera system for on-device facial recognition. A device (e.g., security camera, video doorbell) captures images/video of a person, processes input image frames, detects face images, filters static faces, and aligns a rotation of the face to be upright and frontal. The device then filters low-quality face images and/or images having a large portion of the face occluded. The device computes a face embedding, compares it against a set of locally stored reference embeddings, and sends matching results to cloud services, which, based on the matching result, notifies the device owner whether the observed person is a known person or a stranger. Face detection and recognition computations are performed on device, not at the cloud. No sensitive information is transmitted off device and privacy is thus preserved.

* Decentralized multi-camera system for on-device facial recognition
* Processing of input image frames to detect and align face images
* Filtering of low-quality face images and images with occluded faces
* Comparison of face embeddings with locally stored reference embeddings
* Notification to device owner based on matching results
* On-device face detection and recognition computations for privacy preservation

Potential Applications:
- Home security systems
- Access control systems
- Visitor management systems

Problems Solved:
- Privacy concerns with cloud-based facial recognition
- Efficient and accurate on-device person recognition
- Real-time alerts for device owners

Benefits:
- Enhanced privacy protection
- Quick and reliable person recognition
- Seamless integration with existing security systems

Commercial Applications:
Title: On-Device Facial Recognition System for Enhanced Security
This technology can be used in various commercial settings such as:
- Residential security systems
- Commercial buildings
- Public spaces with access control needs

Prior Art:
Readers can explore prior art related to on-device facial recognition systems in the field of computer vision and artificial intelligence research.

Frequently Updated Research:
Researchers are constantly improving on-device facial recognition algorithms to enhance accuracy and speed in person recognition.

Questions about On-Device Facial Recognition System:
1. How does on-device facial recognition differ from cloud-based facial recognition systems?
On-device facial recognition processes all computations locally, preserving privacy and reducing reliance on cloud services.

2. What are the key advantages of using a decentralized multi-camera system for facial recognition?
A decentralized multi-camera system allows for more comprehensive coverage and accurate person recognition across different angles and lighting conditions.


==Original Abstract Submitted==

the present document describes systems and methods for on-device person recognition and provision of intelligent alerts. the system includes a decentralized multi-camera system for on-device facial recognition. a device (e.g., security camera, video doorbell) captures images/video of a person, processes input image frames, detects face images, filters static faces, and aligns a rotation of the face to be upright and frontal. the device then filters low-quality face images and/or images having a large portion of the face occluded. the device computes a face embedding, compares it against a set of locally stored reference embeddings, and sends matching results to cloud services, which, based on the matching result, notifies the device owner whether the observed person is a known person or a stranger. face detection and recognition computations are performed on device, not at the cloud. no sensitive information is transmitted off device and privacy is thus preserved.



[[Category:G06V40/16]]
[[Category:G06V10/762]]
[[Category:G06V10/94]]
[[Category:CPC_G06V40/172]]