US8374389B2
Active / In Force
US
⏱️ 2.3 years remaining
Iris deblurring method based on global and local iris image statistics
Assignee: Robert Bosch GmbH
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Filing: 2009-02-06
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Granted: 2013-02-12
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Expires: 2029-02-06
Abstract
Abstract A method of identifying a living being includes using a camera to capture a blurred visual image of an iris of the living being. The blurred visual image is digitally unblurred based on a distribution of eye image gradients in an empirically-collected sample of eye images and characteristics of pupil region. The unblurred image is processed to determine an identity of the living being.
Inventors
- 👤 Liu Ren
- 👤 Xinyu Huang
- 👤 Ruigang Yang
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-06 01:28
CPC Classifications
G; G06; G06T; G06T5/00; G06T5/73; H; H04; H04N; H04N23/00; H04N23/60; H04N23/68; H04N23/682; H04N23/683; G06V; G06V40/00; G06V40/10; G06V40/18; G06V40/19; G06T2207/00; G06T2207/30; G06T2207/30004; G06T2207/30041
Claims Analysis
Total Claims: 24
Claim 1
Independent Claim
80 words
A method of identifying a living being, comprising the steps of: using a camera to capture a blurred visual image of an iris of the living being; digitally unblurring the blurred visual image based on a distribution of eye image gradients in an empirically-collected sample of eye images and distributions from characteristics of local regions; processing the unblurred image to determine an identity of the living being; and approximating the distribution of eye image gradients as a quadratic piecewise function.
Claim 2
Independent Claim
80 words
A method of identifying a living being, comprising the steps of: using a camera to capture a blurred visual image of an iris of the living being; digitally unblurring the blurred visual image based on a distribution of eye image gradients in an empirically-collected sample of eye images and distributions from characteristics of local regions; processing the unblurred image to determine an identity of the living being; and approximating the distribution of eye image gradients as a quadratic piecewise function.
Claim 3
Dependent Claim
22 words
The method of claim 1 comprising the further step of computing the distributions from characteristics of local regions from iris images only.
Claim 4
Dependent Claim
15 words
The method of claim 1 wherein the distribution comprises a distribution of iris image gradients.
Claim 5
Independent Claim
86 words
A method of identifying a living being, comprising the steps of: using a camera to capture a blurred visual image of an iris of the living being; digitally unblurring the blurred visual image based on a distribution of eye image gradients in an empirically-collected sample of eye images and distributions from characteristics of local regions; processing the unblurred image to determine an identity of the living being; and forming the distribution of eye image gradients from statistics derived from measurements of a population of eye regions.
Claim 6
Independent Claim
86 words
A method of identifying a living being, comprising the steps of: using a camera to capture a blurred visual image of an iris of the living being; digitally unblurring the blurred visual image based on a distribution of eye image gradients in an empirically-collected sample of eye images and distributions from characteristics of local regions; processing the unblurred image to determine an identity of the living being; and forming the distribution of eye image gradients from statistics derived from measurements of a population of eye regions.
Claim 7
Independent Claim
105 words
A method of identifying a living being, comprising the steps of: using a camera to capture a blurred visual image of an iris of the living being; digitally unblurring the blurred visual image based on a distribution of eye image gradients in an empirically-collected sample of eye images and distributions from characteristics of local regions; processing the unblurred image to determine an identity of the living being; forming the distributions from characteristics of local regions from characteristics of intensity value of pupil region; and forming the distribution of eye image gradients from global intensity value statistics derived from measurements of a population of eye regions.
Claim 8
Independent Claim
105 words
A method of identifying a living being, comprising the steps of: using a camera to capture a blurred visual image of an iris of the living being; digitally unblurring the blurred visual image based on a distribution of eye image gradients in an empirically-collected sample of eye images and distributions from characteristics of local regions; processing the unblurred image to determine an identity of the living being; forming the distributions from characteristics of local regions from characteristics of intensity value of pupil region; and forming the distribution of eye image gradients from global intensity value statistics derived from measurements of a population of eye regions.
Claim 9
Dependent Claim
36 words
The method of claim 5 wherein the distributions from characteristics of intensity value of pupil region include: distribution from local highlight intensity value of pupil region; and distribution from local non-highlight intensity value of pupil region.
Claim 10
Dependent Claim
64 words
The method of claim 6 comprising the further steps of: detecting the highlight regions and the non-highlight regions around pupil region in a captured image; deriving the distribution of the local highlight intensity value from the portion identified as corresponding to the highlight regions; and deriving the distribution of the local non-highlight intensity value from the portion identified as corresponding to the non-highlight regions.
Claim 11
Independent Claim
82 words
A method of identifying a living being, comprising the steps of: using a camera to capture a blurred visual image of an iris of the living being; digitally unblurring the blurred visual image based on: distributions from intensity value of pupil region; and distribution from global intensity value statistics derived from measurements of a population of eye regions; processing the unblurred image to determine an identity of the living being; and modeling the global intensity value statistics as a quadratic piecewise function.
Claim 12
Independent Claim
82 words
A method of identifying a living being, comprising the steps of: using a camera to capture a blurred visual image of an iris of the living being; digitally unblurring the blurred visual image based on: distributions from intensity value of pupil region; and distribution from global intensity value statistics derived from measurements of a population of eye regions; processing the unblurred image to determine an identity of the living being; and modeling the global intensity value statistics as a quadratic piecewise function.
Claim 13
Dependent Claim
25 words
The method of claim 8 wherein the processing step includes matching the unblurred image to a stored image of an iris of the living being.
Claim 14
Dependent Claim
30 words
The method of claim 8 comprising the further step of estimating a blur kernel, the unblurring step including digitally unblurring the blurred visual image based on the estimated blur kernel.
Claim 15
Independent Claim
101 words
A method of identifying a living being, comprising the steps of: using a camera to capture a blurred visual image of an iris of the living being; digitally unblurring the blurred visual image based on: distributions from intensity value of pupil region; and distribution from global intensity value statistics derived from measurements of a population of eye regions; and processing the unblurred image to determine an identity of the living being, wherein distributions from characteristics of intensity value of pupil region include: distribution from local highlight intensity value of pupil region; and distribution from local non-highlight intensity value of pupil region.
Claim 16
Independent Claim
101 words
A method of identifying a living being, comprising the steps of: using a camera to capture a blurred visual image of an iris of the living being; digitally unblurring the blurred visual image based on: distributions from intensity value of pupil region; and distribution from global intensity value statistics derived from measurements of a population of eye regions; and processing the unblurred image to determine an identity of the living being, wherein distributions from characteristics of intensity value of pupil region include: distribution from local highlight intensity value of pupil region; and distribution from local non-highlight intensity value of pupil region.
Claim 17
Dependent Claim
65 words
The method of claim 11 comprising the further steps of: detecting the highlight regions and the non-highlight regions around pupil region in a captured image; deriving the distribution of the local highlight intensity value from the portion identified as corresponding to the highlight regions; and deriving the distribution of the local non-highlight highlight intensity value from the portion identified as corresponding to the non-highlight regions.
Claim 18
Independent Claim
89 words
A method of identifying a living being, comprising the steps of: capturing a blurred visual image of an iris of the living being; digitally unblurring the blurred visual image based on: distribution from local highlight intensity value of pupil region; distribution from local non-highlight intensity value of pupil region; and distribution of global intensity value statistics derived from measurements of a population of eye regions; processing the unblurred image to determine an identity of the living being; and modeling the global intensity value statistics as a quadratic piecewise function.
Claim 19
Independent Claim
89 words
A method of identifying a living being, comprising the steps of: capturing a blurred visual image of an iris of the living being; digitally unblurring the blurred visual image based on: distribution from local highlight intensity value of pupil region; distribution from local non-highlight intensity value of pupil region; and distribution of global intensity value statistics derived from measurements of a population of eye regions; processing the unblurred image to determine an identity of the living being; and modeling the global intensity value statistics as a quadratic piecewise function.
Claim 20
Dependent Claim
16 words
The method of claim 13 wherein the quadratic piecewise function consists of two separate continuous functions.
Claim 21
Dependent Claim
25 words
The method of claim 13 wherein the processing step includes matching the unblurred image to a stored image of an iris of the living being.
Claim 22
Dependent Claim
30 words
The method of claim 13 comprising the further step of estimating a blur kernel, the unblurring step including digitally unblurring the blurred visual image based on the estimated blur kernel.
Claim 23
Independent Claim
131 words
A method of identifying a living being, comprising the steps of: capturing a blurred visual image of an iris of the living being; digitally unblurring the blurred visual image based on: distribution from local highlight intensity value of pupil region; distribution from local non-highlight intensity value ofpupil region; and distribution of global intensity value statistics derived from measurements of a population of eye regions; processing the unblurred image to determine an identity of the living being; detecting the highlight regions and the non-highlight regions around pupil region in a captured image; deriving the distribution of the local highlight intensity value from the portion identified as corresponding to the highlight regions; and deriving the distribution of the local non-highlight highlight intensity value from the portion identified as corresponding to the non-highlight regions.
Claim 24
Independent Claim
131 words
A method of identifying a living being, comprising the steps of: capturing a blurred visual image of an iris of the living being; digitally unblurring the blurred visual image based on: distribution from local highlight intensity value of pupil region; distribution from local non-highlight intensity value ofpupil region; and distribution of global intensity value statistics derived from measurements of a population of eye regions; processing the unblurred image to determine an identity of the living being; detecting the highlight regions and the non-highlight regions around pupil region in a captured image; deriving the distribution of the local highlight intensity value from the portion identified as corresponding to the highlight regions; and deriving the distribution of the local non-highlight highlight intensity value from the portion identified as corresponding to the non-highlight regions.
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USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
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| 3.5 Years (Year 4) | 2016-02-12 | 2016-08-13 | 2017-02-12 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2020-02-12 | 2020-08-13 | 2021-02-12 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2024-02-12 | 2024-08-13 | 2025-02-12 | $7700.00 | Window Closed / Lapsed |