IP Library Granted Patent US 9,412,009
Granted Patent B2
US 9,412,009 · App. 14/550,206 · Granted Aug 9, 2016

Image classification and information retrieval over wireless digital networks and the internet

View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 9,412,009
App. No.
14/550,206
Granted
Aug 9, 2016
Kind
B2
Abstract

A method and system for matching an unknown facial image of an individual with an image of a celebrity using facial recognition techniques and human perception is disclosed herein. The invention provides a internet hosted system to find, compare, contrast and identify similar characteristics among two or more individuals using a digital camera, cellular telephone camera, wireless device for the purpose of returning information regarding similar faces to the user The system features classification of unknown facial images from a variety of internet accessible sources, including mobile phones, wireless camera-enabled devices, images obtained from digital cameras or scanners that are uploaded from PCs, third-party applications and databases. Once classified, the matching person's name, image and associated meta-data is sent back to the user. The method and system uses human perception techniques to weight the feature vectors.

Claims (34)

1. A method for matching an unknown image with a known image, the method comprising:

receiving an unknown facial image from a video camera at an image classification server;

processing the unknown facial image at the image classification server to create a primary feature vector;

comparing the primary feature vector to a plurality of database feature vectors;

matching the primary feature vector to a database feature vector of the plurality of database feature vectors to create matched feature vectors, wherein the database feature vector is for a second facial image;

determining a perception value of the matched feature vectors; and

transmitting the second facial image based on the perception value to a video surveillance system.

2. The method according to claim 1 , wherein the primary feature vector and each of the plurality of database feature vectors are based on one or more of a facial expression, a hair style, a hair color, a facial pose, an eye color, a texture of the face, a color of the face, and facial hair.

3. The method according to claim 1 , wherein the image classification server comprises an input module, a transmission engine, facial recognition software, an input feed, a feature vector database, a perception engine, and an output module.

4. The method according to claim 1 , wherein the perception value ranges from 0% to 100%.

5. A method for matching an unknown image with a known image, the method comprising:

receiving one or more unknown facial images of a person from a video camera at a video surveillance system including an image classification server;

processing the unknown facial images at the image classification server to create a single feature vector or multiple feature vectors;

when there are multiple feature vectors, combining the multiple features vectors into a single feature vector;

comparing the single feature vector to a plurality of database feature vectors in a database;

matching the single feature vector to a database feature vector of the plurality of database feature vectors to create matched feature vectors, wherein the database feature vector is for a known facial image stored in the database;

determining a perception value of the matched feature vectors; and

providing the known facial image based on the perception value at the video surveillance system.

6. The method of claim 5 , further comprising adding the one or more unknown facial images to the database and adding the single feature vector to the database of feature vectors.

7. The method according to claim 5 , wherein the single feature vector and each of the plurality of database feature vectors are based on one or more of a facial expression, a hair style, a hair color, a facial pose, an eye color, a texture of the face, a color of the face, and facial hair.

8. The method according to claim 5 , wherein the plurality of factors further comprise a distance between eyes, a distance between a center of the eyes to a chin, a size and a shape of eyebrows.

9. The method according to claim 5 , wherein the image classification server comprises an input module, a transmission engine, facial recognition software, an input feed, a feature vector database, a perception engine, and an output module.

10. The method according to claim 5 , wherein the perception value ranges from 0% to 100%.

11. A non-transitory computer-readable medium containing instructions, which, when executed on a processor is configured to perform an operation for matching an unknown image with a known image, comprising:

receiving an unknown facial image from a video camera at an image classification server;

processing the unknown facial image at the image classification server to create a primary feature vector;

comparing the primary feature vector to a plurality of database feature vectors;

matching the primary feature vector to a database feature vector of the plurality of database feature vectors to create matched feature vectors, wherein the database feature vector is for a second facial image;

determining a perception value of the matched feature vectors; and

transmitting the second facial image based on the perception value to a video surveillance system.

12. The non-transitory computer-readable medium according to claim 11 , wherein the primary feature vector and each of the plurality of database feature vectors are based on one or more of a facial expression, a hair style, a hair color, a facial pose, an eye color, a texture of the face, a color of the face, and facial hair.

13. The non-transitory computer-readable medium according to claim 11 , wherein the image classification server comprises an input module, a transmission engine, facial recognition software, an input feed, a feature vector database, a perception engine, and an output module.

14. The non-transitory computer-readable medium according to claim 11 , wherein the perception value ranges from 0% to 100%.

15. The non-transitory computer-readable medium according to claim 11 , further comprising transmitting the perception value with the second facial image.

Assignments (8)
NUNC PRO TUNC ASSIGNMENT Recorded Oct 13, 2022
From: AVIGILON PATENT HOLDING 1 CORPORATION
To: MOTOROLA SOLUTIONS, INC.
Reel/Frame 062034/0176 →
RELEASE OF SECURITY INTEREST Recorded Aug 21, 2018
From: HSBC BANK CANADA
To: AVIGILON PATENT HOLDING 1 CORPORATION
Reel/Frame 046895/0803 →
CHANGE OF NAME Recorded Mar 10, 2017
From: 9051147 CANADA INC.
To: AVIGILON PATENT HOLDING 1 CORPORATION
Reel/Frame 041976/0986 →
CORRECTIVE ASSIGNMENT TO CORRECT THE ASSIGNEE NAME PREVIOUSLY RECORDED AT REEL: 034410 FRAME: 0717. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT. Recorded Jun 30, 2015
From: MYERS, CHARLES A.; SHAH, ALEX
To: FACEDOUBLE, INC.
Reel/Frame 036039/0881 →
CORRECTIVE ASSIGNMENT TO CORRECT THE CORRECT TYPOGRAPHICAL ERROR IN ASSIGNOR NAME PREVIOUSLY RECORDED ON REEL 034522 FRAME 133. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT. Recorded Jun 23, 2015
From: FACEDOUBLE, INC.
To: 9051147 CANADA INC.
Reel/Frame 036009/0670 →
SECURITY INTEREST Recorded Apr 8, 2015
From: CANADA INC.
To: HSBC BANK CANADA
Reel/Frame 035387/0176 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 16, 2014
From: FACEDOUBLE CORPORATION
To: 9051147 CANADA INC.
Reel/Frame 034522/0133 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 21, 2014
From: MYERS, CHARLES A.; SHAH, ALEX
To: FACEDOUBLE INCORPORATED
Reel/Frame 034410/0717 →