Biometric object spoof detection based on image intensity variations

• US 10,657,401 B2
• Filed: 02/21/2018
• Issued: 05/19/2020
• Est. Priority Date: 06/06/2017
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: Agrawal, Mudit, Tsukada, Akihiro
• Primary Examiner(s): Chen, Xuemei G
• Application Number:
  US15/901,377
• Publication Number:
  US 20180349721A1
• Time in Patent Office: 818 Days
• CPC Class Codes: G06F 21/32 using biometric data, e.g. fingerprints, iris scans or voiceprints
• CPC Class Codes: G06F 2221/2133 Verifying human interaction, e.g., Captcha
• CPC Class Codes: G06V 40/1388 using image processing
• CPC Class Codes: G06V 40/19 Sensors therefor
• CPC Class Codes: G06V 40/197 Matching; Classification
• CPC Class Codes: G06V 40/45 Detection of the body part being alive

Abstract

Apparatus and methods of biometric object spoof detection are configured to receive at least first and second images, including a biometric object, respectively captured at a first and second time in response to a first and second incident light. The first and second incident light is emitted from at least one light source at substantially a same wavelength, but with different sets of illumination characteristics. Further, the apparatus and method are configured to respectively determine a first set and a corresponding second set of reflection intensity features respectively based on at least a part of the first and second images, and to determine a set of reflection intensity difference features based on an intensity difference therebetween. Additionally, the apparatus and methods are configured to classify the biometric object as being a fake object or a real object based on at least one of the reflection intensity difference features.

Claims

1. A method of biometric object spoof detection, comprising:

receiving a first image, including an eye, captured at a first time in response to a first incident light emitted from a light source at a wavelength with a first set of illumination characteristics;

receiving a second image, including the eye, captured at a second time in response to a second incident light emitted from the light source at the wavelength with a second set of illumination characteristics;

determining a first set of one or more corneal glints in the first image;

determining a second set of one or more corneal glints in the second image, wherein each of the second set of one or more corneal glints corresponds to one of the first set of one or more corneal glints;

determining a glint intensity difference feature between corresponding corneal glints in the first image and the second image; and

classifying the eye as being a fake eye or a real eye based on the glint intensity difference feature.

2. The method of claim 1, further comprising:

determining an overall image intensity difference feature between the first image and the second image; and

wherein classifying the eye as being the fake eye or the real eye is further based on the overall image intensity difference feature.

3. The method of claim 1, further comprising:

determining one or more eye-part intensity difference features between corresponding eye-parts in the first image and the second image; and

wherein classifying the eye as being the fake eye or the real eye is further based on the one or more eye-part intensity difference features.

4. The method of claim 3, wherein the corresponding eye-parts in the first image and the second image include at least one of a pupil, an iris, a sclera, or a periocular region of the first image and the second image.

5. The method of claim 1, wherein determining the glint intensity difference feature includes determining one or more of an intensity amplitude-based metric, an intensity variance-based metric, a local binary pattern-based metric, a pixel intensity-based metric, a local texture-based metric, or an intensity feature-based histogram.

6. The method of claim 1, wherein the first set of illumination characteristics and the second set of illumination characteristics each comprise a different value of at least one of an illumination power, an exposure time, an angle of incidence, or a characteristic resulting in a different signal-to-noise ratio between the first image and the second image.

7. The method of claim 1, wherein the wavelength of the light source comprises one of a plurality of wavelengths within an infrared band of light.

8. The method of claim 1, further comprising:

capturing the first image and the second image at a frame rate such that a position of at least a portion of the eye is substantially similar between the first image and the second image.

9. The method of claim 1, further comprising:

detecting two or more glint-like objects in at least one of the first image and the second image;

determining a size and a shape, respectively, of the two or more glint-like objects;

distinguishing at least one glint from other glass-reflection-based glare based on the size and the shape, respectively, of the two or more glint-like objects.

10. The method of claim 1, further comprising:

performing biometric identification based on the first image or the second image, or based on one or more additional images of the eye received in response to an additional incident light emitted from the light source at the wavelength.

11. A computer device for biometric object spoof detection, comprising:

a processor; and

a memory in communication with the processor, wherein the processor is configured to;

receive a first image, including an eye, captured at a first time in response to a first incident light emitted from a light source at a wavelength with a first set of illumination characteristics;

receive a second image, including the eye, captured at a second time in response to a second incident light emitted from the light source at the wavelength with a second set of illumination characteristics;

determine a first set of one or more corneal glints in the first image;

determine a second set of one or more corneal glints in the second image, wherein each of the second set of one or more corneal glints corresponds to one of the first set of one or more corneal glints;

determine a glint intensity difference feature between corresponding corneal glints in the first image and the second image; and

classify the eye as being a fake eye or a real eye based on the glint intensity difference feature.

12. The computer device of claim 11, wherein the processor is further configured to:

determine an overall image intensity difference feature between the first image and the second image; and

classify the eye as being the fake eye or the real eye further based on the overall image intensity difference feature.

13. The computer device of claim 11, wherein the processor is further configured to:

determine one or more eye-part intensity difference features between corresponding eye-parts in the first image and the second image; and

classify the eye as being the fake eye or the real eye further based on the one or more eye-part intensity difference features.

14. The computer device of claim 13, wherein the corresponding eye-parts in the first image and the second image include at least one of a pupil, an iris, a sclera, or a periocular region of the first image and the second image.

15. The computer device of claim 11, wherein the glint intensity difference feature includes one or more of an intensity amplitude-based metric, an intensity variance-based metric, a local binary pattern-based metric, a pixel intensity-based metric, a local texture-based metric, or an intensity feature-based histogram.

16. The computer device of claim 11, wherein the first set of illumination characteristics and the second set of illumination characteristics each comprise a different value of at least one of an illumination power, an exposure time, an angle of incidence, or a characteristic resulting in a different signal-to-noise ratio between the first image and the second image.

17. The computer device of claim 11, wherein the wavelength of the light source comprises one of a plurality of wavelengths within an infrared band of light.

18. The computer device of claim 11, wherein the processor is further configured to:

capture the first image and the second image at a frame rate such that a position of at least a portion of the eye is substantially similar between the first image and the second image.

19. The computer device of claim 11, wherein the processor is further configured to:

detect two or more glint-like objects in at least one of the first image and the second image;

determine a size and a shape, respectively, of the two or more glint-like objects; and

distinguish at least one glint from other glass-reflection-based glare based on the size and the shape, respectively, of the two or more glint-like objects.

20. The computer device of claim 11, wherein the processor is further configured to:

perform biometric identification based on the first image or the second image, or based on one or more additional images of the eye received in response to an additional incident light emitted from the light source at the wavelength.

21. A non-transitory computer readable medium storing one or more instructions executable by a computer device, the one or more instructions causing the computer device to:

receive a first image, including an eye, captured at a first time in response to a first incident light emitted from a light source at a wavelength with a first set of illumination characteristics;

receive a second image, including the eye, captured at a second time in response to a second incident light emitted from the light source at the wavelength with a second set of illumination characteristics;

determine a first set of one or more corneal glints in the first image;

determine a second set of one or more corneal glints in the second image, wherein each of the second set of one or more corneal glints corresponds to one of the first set of one or more corneal glints;

determine a glint intensity difference feature between corresponding corneal glints in the first image and the second image; and

classify the eye as being a fake eye or a real eye based on the glint intensity difference feature.

22. The non-transitory computer readable medium of claim 21, wherein the one or more instructions further cause the computer device to:

determine an overall image intensity difference feature between the first image and the second image; and

classify the eye as being the fake eye or the real eye based on the overall image intensity difference feature.

23. The non-transitory computer readable medium of claim 21, wherein the one or more instructions further cause the computer device to:

determine one or more eye-part intensity difference features between corresponding eye-parts in the first image and the second image; and

classify the eye as being the fake eye or the real eye based on the one or more eye-part intensity difference features.