Source: http://www.google.com/patents/US8050465?dq=U.S.+patent+number+7,325,728
Timestamp: 2017-01-20 14:50:09
Document Index: 465280112

Matched Legal Cases: ['§119', 'application No. 60', 'application No. 05792584', 'Art. 153', 'application No. 06802192', 'application No. 10163739']

Patent US8050465 - Real-time face tracking in a digital image acquisition device - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inPatentsA database includes an identifier and associated parameters for each of a number of faces to be recognized. A new acquired image from an image stream is received potentially including one or more face regions. Face detection is applied to at least a portion of the acquired image to provide a set of candidate...http://www.google.com/patents/US8050465?utm_source=gb-gplus-sharePatent US8050465 - Real-time face tracking in a digital image acquisition deviceAdvanced Patent SearchTry the new Google Patents, with machine-classified Google Scholar results, and Japanese and South Korean patents.Publication numberUS8050465 B2Publication typeGrantApplication numberUS 12/167,500Publication dateNov 1, 2011Filing dateJul 3, 2008Priority dateAug 11, 2006Fee statusPaidAlso published asUS7403643, US8422739, US20080037838, US20080267461, US20120062761Publication number12167500, 167500, US 8050465 B2, US 8050465B2, US-B2-8050465, US8050465 B2, US8050465B2InventorsMihai Ianculescu, Eran Steinberg, Petronel Bigioi, Peter Corcoran, Mihnea Gangea, Stefan PetrescuOriginal AssigneeDigitalOptics Corporation Europe LimitedExport CitationBiBTeX, EndNote, RefManPatent Citations (432), Non-Patent Citations (143), Referenced by (26), Classifications (25), Legal Events (5) External Links: USPTO, USPTO Assignment, EspacenetReal-time face tracking in a digital image acquisition device
US 8050465 B2Abstract
1. A method of recognizing faces in an image stream with a digital image acquisition device, comprising:
providing a database comprising an identifier and associated parameters for each of a number of one or more faces to be recognized;
receiving a new acquired image from an image stream;
obtaining a location of one or more tracked face regions within said image;
determining if said one or more tracked face regions is optimized for accurate face recognition;
selectively applying face recognition using said database to at least one of said one or more tracked face regions to determine a first identifier for said at least one of said one or more tracked face regions; and
repeating the method one or more times to determine additional identifiers associated with the one or more tracked face regions.
2. The method of claim 1, further comprising identifying one or more of said tracked face regions based on at least a first identifier and a second identical identifier determined for the same face region.
3. The method of claim 1, further comprising failing to identify one or more tracked face regions based on at least a first identifier and a second different identifier determined for the same face region.
4. The method of claim 1 where said determining if said tracked face region is optimized for accurate face recognition includes determining if a face is substantially frontal in aspect, or if the vertical or horizontal rotation, or both, of the face lie within a predetermined range of angles, or if the mean and variance of luminance for a face region lie within predetermined ranges, or combinations thereof.
5. The method of claim 4 wherein the parameters associated with a particular identifier are functions of the vertical or horizontal rotation, or both, or are functions of the mean or variance, or both, of luminance of a tracked face region, or combinations thereof.
6. A method of tracking faces in an image stream with a digital image acquisition device, comprising:
receiving an acquired image from an image stream including one or more face regions;
calculating a corresponding integral image for at least a portion of said image;
applying face detection to at least a portion of said integral image to provide a set of one or more candidate face regions each having a given size and a respective location;
selectively applying face recognition using said database to at least one of said one or more candidate face regions to provide an identifier for a face recognized in a candidate face region; and
storing a portion of said image including the recognized face in association with at least one image of said image stream.
7. The method of claim 6, further comprising sub-sampling said acquired image at a specified resolution to provide a sub-sampled image.
8. The method of claim 7, wherein said calculating said corresponding integral image comprises utilizing said sub-sampled image.
9. The method of claim 6, further comprising merging said set of candidate face regions with one or more previously detected face regions to provide a merged set of candidate face regions of different sizes or locations or both.
10. The method of claim 9, wherein said merging comprises merging a set of candidate face regions for an acquired image with said set of confirmed face regions for a previously acquired image.
11. The method of claim 9, wherein said regions of said acquired image corresponding to a region of said merged set of candidate face regions comprise regions surrounding respective regions of said merged set of candidate face regions.
12. The apparatus of claim 9, wherein said regions of said acquired image corresponding to a region of said merged set of candidate face regions comprise regions surrounding respective regions of said merged set of candidate face regions.
13. The method of claim 6, further comprising, for at least one candidate face region associated with a previous image, enhancing the contrast of the luminance characteristics of the corresponding region of said acquired image.
14. The method of claim 6, wherein said face detection is performed with relaxed face detection parameters.
15. The method of claim 6, wherein said selectively applying face recognition includes selecting only one or more candidate face regions having a frontal alignment for face recognition.
16. The method of claim 6, further comprising selecting only one or more candidate face regions including two eye regions and a mouth region within a given area of the candidate face region.
19. The method of claim 6, comprising:
20. The method of claim 6, wherein said new acquired image comprises a low resolution preview image and wherein said portion of said image including a recognized face is stored in association with a main high resolution image.
21. The method of claim 6, further comprising tracking candidate face regions of different sizes from a plurality of images of said image stream.
22. The method of claim 6, wherein said applying fixed size face detection comprises applying a cascade of Haar classifiers of a fixed size to said integral image.
23. The method of claim 6, further comprising responsive to said acquired image being captured with a flash, analyzing one or more regions of said acquired image corresponding to said tracked candidate face regions for red-eye defect.
24. An image acquisition and processing apparatus including a lens and an image sensor for acquiring digital images, and one or more processors and one or more digital storage media having digitally-encoded instructions embedded therein for programming the one or more processors to perform a method of recognizing faces in an image stream, the method comprising:
receiving a new image from an image stream;
obtaining a location of one or more tracked face region within said image;
determining if said tracked face region is optimized for accurate face recognition;
selectively applying face recognition to at least one of said one or more tracked face regions to determine a first identifier for said face region; and
25. The apparatus of claim 24, wherein the method further comprises identifying one or more of said tracked face regions based on at least a first identifier and a second identical identifier determined for the same face region.
26. The apparatus of claim 24, wherein the method further comprises failing to identify one or more tracked face regions based on at least a first identifier and a second different identifier determined for the same face region.
27. The apparatus of claim 24 where said determining if said tracked face region is optimized for accurate face recognition includes determining if a face is substantially frontal in aspect, or if the vertical or horizontal rotation, or both, of the face lie within a predetermined range of angles, or if the mean and variance of luminance for a face region lie within predetermined ranges, or combinations thereof.
28. The apparatus of claim 27 wherein the parameters associated with a particular identifier are functions of the vertical or horizontal rotation, or both, or are functions of the mean or variance, or both, of luminance of a tracked face region, or combinations thereof.
29. An image acquisition and processing apparatus including a lens and an image sensor for acquiring digital images, and one or more processors and one or more digital storage media having digitally-encoded instructions embedded therein for programming the one or more processors to perform a method of recognizing faces in an image stream, the method comprising:
30. The apparatus of claim 29, wherein the method further comprises sub-sampling said acquired image at a specified resolution to provide a sub-sampled image.
31. The apparatus of claim 30, wherein said calculating said corresponding integral image comprises utilizing said sub-sampled image.
32. The apparatus of claim 29, wherein the method further comprises merging said set of candidate face regions with one or more previously detected face regions to provide a merged set of candidate face regions of different sizes or locations or both.
33. The apparatus of claim 32, wherein said merging comprises merging a set of candidate face regions for an acquired image with said set of confirmed face regions for a previously acquired image.
34. The apparatus of claim 29, wherein the method further comprises, for at least one candidate face region associated with a previous image, enhancing the contrast of the luminance characteristics of the corresponding region of said acquired image.
35. The apparatus of claim 29, wherein said face detection is performed with relaxed face detection parameters.
36. The apparatus of claim 29, wherein said selectively applying face recognition includes selecting only one or more candidate face regions having a frontal alignment for face recognition.
37. The apparatus of claim 29, wherein the method further comprises selecting only one or more candidate face regions including two eye regions and a mouth region within a given area of the candidate face region.
38. The apparatus of claim 29, wherein the method further comprises:
39. The apparatus of claim 29, wherein the method further comprises:
40. The apparatus of claim 29, wherein the method further comprises:
41. The apparatus of claim 29, wherein said new acquired image comprises a low resolution preview image and wherein said portion of said image including a recognized face is stored in association with a main high resolution image.
42. The apparatus of claim 29, wherein the method further comprises tracking candidate face regions of different sizes from a plurality of images of said image stream.
43. The apparatus of claim 29, wherein said applying fixed size face detection comprises applying a cascade of Haar classifiers of a fixed size to said integral image.
44. The apparatus of claim 29, wherein the method further comprises responsive to said acquired image being captured with a flash, analyzing one or more regions of said acquired image corresponding to said tracked candidate face regions for red-eye defect.
45. One or more non-transitory, computer readable media having digitally-encoded instructions embedded therein for programming one or more processors to perform a method of recognizing faces in an image stream, the method comprising:
46. The one or more processor readable storage devices of claim 45, wherein the method further comprises identifying one or more of said tracked face regions based on at least a first identifier and a second identical identifier determined for the same face region.
47. The one or more processor readable storage devices of claim 45, wherein the method further comprises failing to identify one or more tracked face regions based on at least a first identifier and a second different identifier determined for the same face region.
48. The one or more processor readable storage devices of claim 45 where said determining if said tracked face region is optimized for accurate face recognition includes determining if a face is substantially frontal in aspect, or if the vertical or horizontal rotation, or both, of the face lie within a predetermined range of angles, or if the mean and variance of luminance for a face region lie within predetermined ranges, or combinations thereof.
49. The one or more processor readable storage devices of claim 48 wherein the parameters associated with a particular identifier are functions of the vertical or horizontal rotation, or both, or are functions of the mean or variance, or both, of luminance of a tracked face region, or combinations thereof.
50. One or more non-transitory, computer readable media having digitally-encoded instructions embedded therein for programming one or more processors to perform a method of tracking and recognizing faces in an image stream, the method comprising:
51. The one or more processor readable storage devices of claim 50, wherein the method further comprises sub-sampling said acquired image at a specified resolution to provide a sub-sampled image.
52. The one or more processor readable storage devices of claim 51, wherein said calculating said corresponding integral image comprises utilizing said sub-sampled image.
53. The one or more processor readable storage devices of claim 50, wherein the method further comprises merging said set of candidate face regions with one or more previously detected face regions to provide a merged set of candidate face regions of different sizes or locations or both.
54. The one or more processor readable storage devices of claim 53, wherein said merging comprises merging a set of candidate face regions for an acquired image with said set of confirmed face regions for a previously acquired image.
55. The one or more processor readable storage devices of claim 53, wherein said regions of said acquired image corresponding to a region of said merged set of candidate face regions comprise regions surrounding respective regions of said merged set of candidate face regions.
56. The one or more processor readable storage devices of claim 50, wherein the method further comprises, for at least one candidate face region associated with a previous image, enhancing the contrast of the luminance characteristics of the corresponding region of said acquired image.
57. The one or more processor readable storage devices of claim 50, wherein said face detection is performed with relaxed face detection parameters.
58. The one or more processor readable storage devices of claim 50, wherein said selectively applying face recognition includes selecting only one or more candidate face regions having a frontal alignment for face recognition.
59. The one or more processor readable storage devices of claim 50, wherein the method further comprises selecting only one or more candidate face regions including two eye regions and a mouth region within a given area of the candidate face region.
60. The one or more processor readable storage devices of claim 50, wherein the method further comprises:
61. The one or more processor readable storage devices of claim 50, wherein the method further comprises:
62. The one or more processor readable storage devices of claim 50, wherein the method further comprises:
63. The one or more processor readable storage devices of claim 50, wherein said new acquired image comprises a low resolution preview image and wherein said portion of said image including a recognized face is stored in association with a main high resolution image.
64. The one or more processor readable storage devices of claim 50, wherein the method further comprises tracking candidate face regions of different sizes from a plurality of images of said image stream.
65. The one or more processor readable storage devices of claim 50, wherein said applying fixed size face detection comprises applying a cascade of Haar classifiers of a fixed size to said integral image.
66. The one or more processor readable storage devices of claim 50, wherein the method further comprises responsive to said acquired image being captured with a flash, analyzing one or more regions of said acquired image corresponding to said tracked candidate face regions for red-eye defect.
This application is a Continuation of U.S. patent application Ser. No. 11/753,397 filed on May 24, 2007, now U.S. Pat. No. 7,403,643, which is a Continuation-in-Part (CIP) of U.S. patent application Ser. No. 11/464,083, filed Aug. 11, 2006, now U.S. Pat. No. 7,315,631, and also claims the benefit of priority under 35 USC §119 to U.S. provisional patent application No. 60/829,127, filed Oct. 11, 2006.
This user interface approach also enables much faster image annotation and grouping of images associated with certain people, or groups of people, than conventional approaches to image annotation. For example, a user may select a “known person” and quickly run through several hundred images, selecting images associated with that person by scanning through the set of face regions displayed with each main image. Even where a person is in the background in a main image or where the face region in the main image is not clear, the saved face candidate regions will be larger and of good frontal pose and illumination thus being easily identified by the user.
In one embodiment this alignment employs an eye detector and a mouth detector. If both eyes are correctly detected and are located in a symmetric position in relation to the left-hand and right-hand edges of the face candidate region then the face may be determined to be frontally aligned along the vertical axis. It the mouth region is also detected and is located at a distance within a certain percentage range of the lower edge of the face candidate region, then frontal alignment along the horizontal axis may also be confirmed and the face candidate is determined to be frontally aligned.
In another embodiment an Active Appearance Face Model such as is disclosed in “Fast and Reliable Active Appearance Model Search for 3-D Face Tracking”, F Dornaika and J Ahlberg, IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, Vol 34, No. 4, pg 1838-1853, August 2004, or the original paper by T F Cootes et al “Active Appearance Models” Proc. European Conf. Computer Vision, 1998, pp 484-498, is applied to the face candidate region and converges on the true shape of the face region. Parameters of the converged AAM model indicate horizontal and vertical rotation of the face and where these are within a certain range of rotation, e.g., +/−10 degrees rotation, the face is determined to be frontally aligned.
In another embodiment, various chain classifiers for various rotations of the face are employed. The best face (frontal position) is saved when the frontal classifier chain is chosen for detection. Alternatively (if no frontal classifier chain yields detection), the closest classifier chain to frontal detection is chosen and an indication of the angle at which this classifier chain has been trained for is provided. A further normalization stage (in preparation for face recognition) can then “rotate” the face.
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H04N5/232, H04N1/2112, G06K9/6857, H04N2201/3205, H04N1/32128, G06K9/6257European ClassificationG06K9/00F1, H04N1/21B3, H04N1/32C17, H04N5/232, G06K9/00F1V, H04N1/32C15B, H04N5/232H, G06K9/00F1C, G06K9/68B1Legal EventsDateCodeEventDescriptionSep 2, 2008ASAssignmentOwner name: FOTONATION VISION LIMITED, IRELANDFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:IANCULESCU, MIHAI;BIGIOI, PETRONEL;GANGEA, MIHNEA;AND OTHERS;REEL/FRAME:021468/0516;SIGNING DATES FROM 20070724 TO 20070827Owner name: FOTONATION VISION LIMITED, IRELANDFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:IANCULESCU, MIHAI;BIGIOI, PETRONEL;GANGEA, MIHNEA;AND OTHERS;SIGNING DATES FROM 20070724 TO 20070827;REEL/FRAME:021468/0516Dec 31, 2010ASAssignmentOwner name: TESSERA TECHNOLOGIES IRELAND LIMITED, IRELANDFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:FOTONATION VISION LIMITED;REEL/FRAME:025567/0181Effective date: 20101001Aug 31, 2011ASAssignmentOwner name: DIGITALOPTICS CORPORATION 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