IP Library Granted Patent US 11,508,188
Granted Patent B2
US 11,508,188 · App. 17/022,451 · Granted Nov 22, 2022

Method and apparatus for testing liveness

Inventors: Hana Lee (Suwon-si, KR); Youngjun Kwak (Seoul, KR); Sungheon Park (Suwon-si, KR); Hyeongwook Yang (Suwon-si, KR); Byung In Yoo (Seoul, KR); Juwoan Yoo (Anyang-si, KR); Solae Lee (Suwon-si, KR); Yong-Il Lee (Daejeon, KR); Jiho Choi (Seoul, KR)
Assignee: Samsung Electronics Co., Ltd.
G06V40/45G06N3/02G06V10/56G06V10/60
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 11,508,188
App. No.
17/022,451
Granted
Nov 22, 2022
Kind
B2
Abstract

Disclosed is a method and apparatus for testing a liveness, where the liveness test method includes receiving a color image and a photodiode (PD) image of an object from an image sensor comprising a pixel formed of a plurality of PDs, preprocessing the color image and the PD image, and determining a liveness of the object by inputting a result of preprocessing the color image and a result of preprocessing the PD image into a neural network.

Claims (55)

1. A liveness test method, comprising:

receiving a color image and a photodiode (PD) image of an object from an image sensor comprising a pixel formed of a plurality of PDs;

preprocessing the color image and the PD image; and

determining a liveness of the object by inputting a result of preprocessing the color image into a first neural network and a result of preprocessing the PD image into a second neural network,

wherein the determining comprises:

calculating a liveness score based on a first output of the first neural network with respect to the color image and a second output of the second neural network with respect to the PD image; and

determining the liveness of the object to be live in response to the liveness score being greater than or equal to a threshold, and

wherein the calculating comprises obtaining the liveness score by adding a result of multiplying the first output by a first weight of the color image to a result of multiplying the second output by a second weight of the PD image, and

wherein the second weight is based on a distance between the image sensor and the object.

2. The liveness test method of claim 1 , wherein the second weight is based on a proportion of a region of the object to the PD image.

3. The liveness test method of claim 1 , wherein the second weight is based on illuminance information of the image sensor.

4. The liveness test method of claim 1 , wherein the determining comprises:

testing a first liveness of the object based on a first output of the first neural network with respect to the result of the inputting of the preprocessed color image;

determining the liveness of the object to be fake, in response to a result of the testing of the first liveness being fake; and

testing a second liveness of the object based on a second output of the second neural network with respect to the result of the inputting of the preprocessed PD image, in response to the result of the testing of the first liveness being live.

5. The liveness test method of claim 1 , wherein the determining comprises:

testing a first liveness of the object based on a first output of the first neural network with respect to the result of the inputting of the preprocessed color image;

determining the liveness of the object to be live, in response to a result of the testing of the first liveness being live; and

testing a second liveness of the object based on a second output of the second neural network with respect to the result of the inputting of the preprocessed PD image, in response to the result of the testing of the first liveness being fake.

6. The liveness test method of claim 1 , wherein the determining comprises:

testing a first liveness of the object based on a first output of the second neural network with respect to the result of the inputting of the preprocessed PD image;

determining the liveness of the object to be fake, in response to a result of the testing of the first liveness being fake; and

testing a second liveness of the object based on a second output of the first neural network with respect to the result of the inputting of the preprocessed color image, in response to the result of the testing of the first liveness being live.

7. The liveness test method of claim 1 , wherein the determining comprises:

testing a first liveness of the object based on a first output of the second neural network with respect to the result of the inputting of the preprocessed PD image;

determining the liveness of the object to be live, in response to a result of the testing of the first liveness being live; and

testing a second liveness of the object based on a second output of the first neural network with respect to the result of the inputting of the preprocessed color image, in response to the result of the testing of the first liveness being fake.

8. A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the liveness test method of claim 1 .

9. A liveness test apparatus, comprising:

a memory configured to store a color image and a photodiode (PD) image of an object received from an image sensor including a pixel formed of a plurality of PDs; and

a processor configured to:

preprocess the color image and the PD image, and

determine a liveness of the object by inputting a result of preprocessing the color image into a first neural network and a result of preprocessing the PD image into a second neural network,

wherein the processor is further configured to:

calculate a liveness score based on a first output of the first neural network with respect to the color image and a second output of the second neural network with respect to the PD image, and

determine the liveness of the object to be live in response to the liveness score being greater than or equal to a threshold,

wherein the processor is further configured to obtain the liveness score by adding a result of multiplying the first output by a first weight of the color image to a result of multiplying the second output by a second weight of the PD image, and

wherein the second weight is based on a proportion of a region of the object to the PD image.

10. The liveness test apparatus of claim 9 , wherein the second weight is based on illuminance information of the PD image.

11. The liveness test apparatus of claim 9 , wherein the processor is further configured to:

test a first liveness of the object based on a first output of the first neural network with respect to the result of the inputting of the preprocessed color image,

determine the liveness of the object to be fake, in response to a result of the testing being fake, and

test a second liveness of the object based on a second output of the second neural network with respect to the result of the inputting of the preprocessed PD image, in response to the result of the testing being live.

12. The liveness test apparatus of claim 9 , wherein the processor is further configured to:

test a first liveness of the object based on a first output of the first neural network with respect to the result of the inputting of the preprocessed color image,

determine the liveness of the object to be live, in response to a result of the testing of the first liveness being live, and

test a second liveness of the object based on a second output of the second neural network with respect to the result of the inputting of the preprocessed PD image, in response to the result of the testing of the first liveness being fake.

13. The liveness test apparatus of claim 9 , wherein the processor is further configured to:

test a first liveness of the object based on a first output of the second neural network with respect to the result of the inputting of the preprocessed PD image,

determine the liveness of the object to be fake, in response to a result of the testing of the first liveness being fake, and

test a second liveness of the object based on a second output of the first neural network with respect to the result of the inputting of the preprocessed color image, in response to the result of the testing being live.

14. The liveness test apparatus of claim 9 , wherein the processor is further configured to:

test a first liveness of the object based on a first output of the second neural network with respect to the result of the inputting of the preprocessed PD image,

determine the liveness of the object to be live, in response to a result of the testing of the first liveness being live, and

test a second liveness of the object based on a second output of the first neural network with respect to the result the inputting of the preprocessed color image, in response to the result of the testing of the first liveness being fake.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 16, 2020
From: LEE, HANA; KWAK, YOUNGJUN; PARK, SUNGHEON; YANG, HYEONGWOOK; YOO, BYUNG IN; YOO, JUWOAN; LEE, SOLAE; LEE, YONG-IL; CHOI, JIHO
To: SAMSUNG ELECTRONICS CO., LTD.
Reel/Frame 053788/0117 →
Priority Claims (1)
KR 10-2020-0046273 · Apr 16, 2020 · national
Continuity (1)
Related Publication 20210326616A1 · Oct 21, 2021
Cited By (2)
US 12,475,689 US 12,567,237