DEVICES, METHODS AND SYSTEMS FOR BIOMETRIC USER RECOGNITION UTILIZING NEURAL NETWORKS

• US 20160335512A1
• Filed: 05/09/2016
• Published: 11/17/2016
• Est. Priority Date: 05/11/2015
• Status: Active Grant

• Current Assignee: Magic Leap, Inc.
• Original Assignee: Magic Leap, Inc.
• Inventors: Bradski, Gary R.
• Granted Patent: US 10,275,902 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06T 7/62 of area, perimeter, diameter or volume
• CPC Class Codes: G06V 10/454 Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
• CPC Class Codes: G06V 10/82 using neural networks
• CPC Class Codes: G06V 40/15 Biometric patterns based on physiological signals, e.g. heartbeat, blood flow
• CPC Class Codes: G06V 40/18 Eye characteristics, e.g. of the iris
• CPC Class Codes: G06V 40/197 Matching; Classification

Abstract

A user identification system includes an image recognition network to analyze image data and generate shape data based on the image data. The system also includes a generalist network to analyze the shape data and generate general category data based on the shape data. The system further includes a specialist network to compare the general category data with a characteristic to generate narrow category data. Moreover, the system includes a classifier layer including a plurality of nodes to represent a classification decision based on the narrow category data.

Claims

1. A user identification system, comprising:

an image recognition network to analyze image data and generate shape data based on the image data;

a generalist network to analyze the shape data and generate general category data based on the shape data;

a specialist network to compare the general category data with a characteristic to generate narrow category data; and

a classifier layer including a plurality of nodes to represent a classification decision based on the narrow category data.

2. The system of claim 1, further comprising a back propagation neural network including a plurality of layers.

3. The system of claim 2, wherein the back propagation neural network also includes error suppression and learning elevation.

4. The system of claim 1, further comprising an ASIC encoded with the image recognition network.

5. The system of claim 1, wherein the specialist network comprises a back propagation network including a plurality of layers.

6. The system of claim 1, further comprising a tuning layer to modify the general category data based on user eye movements.

7. A method of identifying a user of a system, comprising:

analyzing image data;

generating shape data based on the image data;

analyzing the shape data;

generating general category data based on the shape data;

generating narrow category data by comparing the general category data with a characteristic; and

generating a classification decision based on the narrow category data.

8. The method of claim 7, further comprising identifying an error in a piece of data.

9. The method of claim 8, further comprising suppressing the piece of data in which the error is identified.

10. The method of claim 7, wherein analyzing the image data comprises scanning a plurality of pixel of the image data.

11. The method of claim 7, wherein the image data corresponds to an eye of the user.

12. The method of claim 7, wherein the characteristic is from a known potentially confusing mismatched individual.

13. The method of claim 7, wherein the characteristic is selected from the group consisting of eyebrow shape and eye shape.

14. The method of claim 7, further comprising generating a network of characteristics, wherein each respective characteristic of the network is associated with a potentially confusing mismatched individual in a database.

15. The method of claim 14, wherein the network of characteristics is generated when the system is first calibrated for the user.

16. The method of claim 7, further comprising tracking the user'"'"'s eye movements over time.

17. The method of claim 16, further comprising modifying the general category data based on the eye movements of the user before comparing the general category data with the limitation.

18. The method of claim 16, further comprising modifying the general category data to conform to a variance resulting from the eye movements of the user.

19. A computer program product embodied in a non-transitory computer readable medium, the computer readable medium having stored thereon a sequence of instructions which, when executed by a processor causes the processor to execute a method for identifying a user of a system, the method comprising:

analyzing image data;

generating shape data based on the image data;

analyzing the shape data;

generating general category data based on the shape data;

generating narrow category data by comparing the general category data with a characteristic; and

generating a classification decision based on the narrow category data.