Detecting Red Eye Filter and Apparatus Using Meta-Data

Abstract

A method of filtering a red-eye phenomenon from an acquired digital image including a multiplicity of pixels indicative of color, the pixels forming various shapes of the image, includes analyzing meta-data information, determining one or more regions within the digital image suspected as including red eye artifact, and determining, based at least in part on the meta-data analysis, whether the regions are actual red eye artifact. The meta-data information may include information describing conditions under which the image was acquired, captured and/or digitized, acquisition device-specific information, and/film information.

What is claimed is: 
     
         1 . A method of detecting and correcting an eye defect within an acquired digital image comprising a multiplicity of pixels indicative of luminance and color, the pixels forming various shapes within the image, the method comprising:
 acquiring a digital image including said multiplicity of pixels indicative of luminance and color;   detecting a candidate eye defect region based on color, luminance or shape, or combinations thereof, within the digital image;   determining a size of the candidate eye defect region;   analyzing anthropometric information including statistics relating to at least one relationship between said size of said candidate eye defect region and a location of a second detected eye, lips, nostrils or a surrounding face, or combinations thereof;   determining a distance between an image acquisition device that acquired the digital image and an eye of a subject that comprises said eye defect region; and   determining, based at least in part on said distance, said size and said anthropometric information, whether said candidate eye defect region is suspected as including an eye defect region.   
     
     
         2 . The method of  claim 1 , further comprising analyzing meta-data information including image acquisition device-specific information, including f-stop, aperture, exposure, gain, white balance or color transformation, or combinations thereof, and wherein the determining whether said candidate eye defect region is suspected as including an eye defect region if further based on said meta-data. 
     
     
         3 . The method of  claim 2 , wherein the meta-data further includes information describing conditions under which the image was acquired. 
     
     
         4 . A method of determining an age of a face within an acquired digital image comprising a multiplicity of pixels indicative of luminance and color, the pixels forming various shapes within the image, the method comprising:
 acquiring a digital image including said multiplicity of pixels indicative of luminance and color;   detecting a candidate eye defect region based on color, luminance or shape, or combinations thereof, within the digital image;   determining a size of the candidate eye defect region;   analyzing anthropometric information including statistics relating to at least one relationship between said size of said candidate eye defect region and a location of a second detected eye, lips, nostrils or a surrounding face, or combinations thereof;   determining a distance between an image acquisition device that acquired the digital image and an eye of a subject that comprises said candidate eye defect region; and   determining, based at least in part on said distance, said size and said anthropometric information, an age of a subject whose eye comprises said candidate eye defect region.   
     
     
         5 . The method of  claim 4 , further comprising analyzing meta-data information including image acquisition device-specific information, including f-stop, aperture, exposure, gain, white balance or color transformation, or combinations thereof, and wherein the determining said age of said subject whose eye comprises said candidate eye defect region is further based on said meta-data. 
     
     
         6 . The method of  claim 5 , wherein the meta-data further includes information describing conditions under which the image was acquired. 
     
     
         7 . The method of  claim 4 , further comprising determining, based at least in part on said distance, said size and said anthropometric information, whether said candidate eye defect region is suspected as including an eye defect region. 
     
     
         8 . One or more non-transitory processor-readable media having code embodied therein to program one or more processors to perform a method of detecting and correcting an eye defect within an acquired digital image comprising a multiplicity of pixels indicative of luminance and color, the pixels forming various shapes within the image, wherein the method comprises:
 acquiring a digital image including said multiplicity of pixels indicative of luminance and color;   detecting a candidate eye defect region based on color, luminance or shape, or combinations thereof, within the digital image;   determining a size of the candidate eye defect region;   analyzing anthropometric information including statistics relating to at least one relationship between said size of said candidate eye defect region and a location of a second detected eye, lips, nostrils or a surrounding face, or combinations thereof;   determining a distance between an image acquisition device that acquired the digital image and an eye of a subject that comprises said eye defect region; and   determining, based at least in part on said distance, said size and said anthropometric information, whether said candidate eye defect region is suspected as including an eye defect region.   
     
     
         9 . The one or more non-transitory processor-readable media of  claim 8 , wherein the method further comprises analyzing meta-data information including image acquisition device-specific information, including f-stop, aperture, exposure, gain, white balance or color transformation, or combinations thereof, and wherein the determining whether said candidate eye defect region is suspected as including an eye defect region if further based on said meta-data. 
     
     
         10 . The one or more non-transitory processor-readable media of  claim 9 , wherein the meta-data further includes information describing conditions under which the image was acquired. 
     
     
         11 . One or more non-transitory processor-readable media having code embodied therein to program one or more processors to perform a method of determining an age of a face within an acquired digital image comprising a multiplicity of pixels indicative of luminance and color, the pixels forming various shapes within the image, wherein the method comprises:
 acquiring a digital image including said multiplicity of pixels indicative of luminance and color;   detecting a candidate eye defect region based on color, luminance or shape, or combinations thereof, within the digital image;   determining a size of the candidate eye defect region;   analyzing anthropometric information including statistics relating to at least one relationship between said size of said candidate eye defect region and a location of a second detected eye, lips, nostrils or a surrounding face, or combinations thereof;   determining a distance between an image acquisition device that acquired the digital image and an eye of a subject that comprises said candidate eye defect region; and   determining, based at least in part on said distance, said size and said anthropometric information, an age of a subject whose eye comprises said candidate eye defect region.   
     
     
         12 . The one or more non-transitory processor-readable media of  claim 11 , wherein the method further comprises analyzing meta-data information including image acquisition device-specific information, including f-stop, aperture, exposure, gain, white balance or color transformation, or combinations thereof, and wherein the determining said age of said subject whose eye comprises said candidate eye defect region is further based on said meta-data. 
     
     
         13 . The one or more non-transitory processor-readable media of  claim 12 , wherein the meta-data further includes information describing conditions under which the image was acquired. 
     
     
         14 . The one or more non-transitory processor-readable media of  claim 11 , wherein the method further comprises determining, based at least in part on said distance, said size and said anthropometric information, whether said candidate eye defect region is suspected as including an eye defect region. 
     
     
         15 . A digital image acquisition device, comprising:
 a lens,   an image sensor,   a processor, and   a memory having program code embodied therein for programming the processor to perform a method of detecting and correcting an eye defect within an acquired digital image comprising a multiplicity of pixels indicative of luminance and color, the pixels forming various shapes within the image, wherein the method comprises:
 acquiring a digital image including said multiplicity of pixels indicative of luminance and color; 
 detecting a candidate eye defect region based on color, luminance or shape, or combinations thereof, within the digital image; 
 determining a size of the candidate eye defect region; 
 analyzing anthropometric information including statistics relating to at least one relationship between said size of said candidate eye defect region and a location of a second detected eye, lips, nostrils or a surrounding face, or combinations thereof; 
 determining a distance between an image acquisition device that acquired the digital image and an eye of a subject that comprises said eye defect region; and 
 determining, based at least in part on said distance, said size and said anthropometric information, whether said candidate eye defect region is suspected as including an eye defect region. 
   
     
     
         16 . The device of  claim 15 , wherein the method further comprises analyzing meta-data information including image acquisition device-specific information, including f-stop, aperture, exposure, gain, white balance or color transformation, or combinations thereof, and wherein the determining whether said candidate eye defect region is suspected as including an eye defect region if further based on said meta-data. 
     
     
         17 . The device of  claim 16 , wherein the meta-data further includes information describing conditions under which the image was acquired. 
     
     
         18 . A digital image acquisition device, comprising:
 a lens,   an image sensor,   a processor, and   a memory having program code embodied therein for programming the processor to perform a method of determining an age of a face within an acquired digital image comprising a multiplicity of pixels indicative of luminance and color, the pixels forming various shapes within the image, wherein the method comprises:
 acquiring a digital image including said multiplicity of pixels indicative of luminance and color; 
 detecting a candidate eye defect region based on color, luminance or shape, or combinations thereof, within the digital image; 
 determining a size of the candidate eye defect region; 
 analyzing anthropometric information including statistics relating to at least one relationship between said size of said candidate eye defect region and a location of a second detected eye, lips, nostrils or a surrounding face, or combinations thereof; 
 determining a distance between an image acquisition device that acquired the digital image and an eye of a subject that comprises said candidate eye defect region; and 
 determining, based at least in part on said distance, said size and said anthropometric information, an age of a subject whose eye comprises said candidate eye defect region. 
   
     
     
         19 . The one or more non-transitory processor-readable media of  claim 18 , wherein the method further comprises analyzing meta-data information including image acquisition device-specific information, including f-stop, aperture, exposure, gain, white balance or color transformation, or combinations thereof, and wherein the determining said age of said subject whose eye comprises said candidate eye defect region is further based on said meta-data. 
     
     
         20 . The one or more non-transitory processor-readable media of  claim 19 , wherein the meta-data further includes information describing conditions under which the image was acquired. 
     
     
         21 . The one or more non-transitory processor-readable media of  claim 18 , wherein the method further comprises determining, based at least in part on said distance, said size and said anthropometric information, whether said candidate eye defect region is suspected as including an eye defect region.