Apparatus for and method of feature extraction for image recognition
First Claim
1. A system for performing image recognition, the system comprising:
- an image input device which inputs a first image;
a database having a set of reference images; and
a comparison unit which receives the first image having been divided into first image sub-regions, compares the first image sub-regions with corresponding reference image sub-regions of the reference images, and determines based on the comparison which of the reference images has a greatest correlation to the first image based on the comparisons between the reference image sub-regions and the first image sub-regions.
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Abstract
An apparatus for and method of performing a most informative feature extraction (MIFE) method in which a facial image is separated into sub-regions, and each sub-region makes individual contribution for performing facial recognition. Specifically, each sub-region is subjected to a sub-region based adaptive gamma (SadaGamma) correction or sub-region based histogram equalization (SHE) in order to account for different illuminations and expressions. A set of reference images is also divided into sub-regions and subjected to the SadaGamma correction or SHE. A comparison is made between the each corrected sub-region and each corresponding sub-region of the reference images. Based upon the comparisons made individually for the sub-regions of the facial image, one of the stored reference images having the greatest correspondence is chosen. While usable individually, using the MIFE and/or SadaGamma correction or SHE together achieves a lower error ratio in face recognition under different expressions, illuminations and occlusions.
43 Citations
55 Claims
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1. A system for performing image recognition, the system comprising:
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an image input device which inputs a first image; a database having a set of reference images; and a comparison unit which receives the first image having been divided into first image sub-regions, compares the first image sub-regions with corresponding reference image sub-regions of the reference images, and determines based on the comparison which of the reference images has a greatest correlation to the first image based on the comparisons between the reference image sub-regions and the first image sub-regions. - View Dependent Claims (2)
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3. A system for performing image recognition, the system comprising:
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an image input device which inputs a first image; a database having a set of reference images; and a comparison unit which receives the first image having been divided into first image sub-regions, compares the first image sub-regions with corresponding reference image sub-regions of the reference images, and determines based on the comparison which of the reference images has a greatest correlation to the first image based on the comparisons between the reference image sub-regions and the first image sub-regions, wherein the comparison unit, after each comparison of the first and corresponding reference image sub-regions, stores an identification of which of the reference images has the sub-region with a greatest correspondence with the first image sub-region, and after a predetermined number of the first and reference image sub-regions have been compared, reviews the stored identifications to determine which of the reference images has a greatest number of reference image sub-regions corresponding to the first image sub-regions in order to determine which of the reference images has the greatest correlation to the first image. - View Dependent Claims (4, 5, 6)
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7. A system for performing image recognition, the system comprising:
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an image input device which inputs a first image; a database having a set of reference images; and a comparison unit which receives the first image having been divided into first image sub-regions, compares the first image sub-regions with corresponding reference image sub-regions of the reference images, and determines based on the comparison which of the reference images has a greatest correlation to the first image based on the comparisons between the reference image sub-regions and the first image sub-regions, wherein each of the first image sub-regions has a height h and a width w, the first image has a height H and a width W, and a number of first image sub-regions is int(H/h)*int(W/w). - View Dependent Claims (8)
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9. A system for performing image recognition, the system comprising:
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an image input device which inputs a first image; a database having a set of reference images; and a comparison unit which receives the first image having been divided into first image sub-regions, compares the first image sub-regions with corresponding reference image sub-regions of the reference images, and determines based on the comparison which of the reference images has a greatest correlation to the first image based on the comparisons between the reference image sub-regions and the first image sub-regions, wherein the comparison unit compares the first and reference images sub-regions by, for a jth image sub-region of the first image and the reference images, calculating a label I for the jth sub-region as and determining a D dimensional decision matrix Y=[y′
1, y′
2, . . . y′
D] aszjx is the jth image sub-region of the first image, xjk is the jth image sub-region of the reference images, D is a number of sub-regions, and N is a number of reference images.
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10. A system for performing image recognition, the system comprising:
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an image input device which inputs a first image; a database having a set of reference images; a comparison unit which receives the first image having been divided into first image sub-regions, compares the first image sub-regions with corresponding reference image sub-regions of the reference images, and determines based on the comparison which of the reference images has a greatest correlation to the first image based on the comparisons between the reference image sub-regions and the first image sub-regions; and a correction unit which compares each of the first image sub-regions with a mean for the corresponding first image sub-region to remove an influence of illumination and/or occlusion in each of the first image sub-regions to produce a corrected first image, wherein the comparison unit compares the corrected first image sub-regions with the reference image sub-regions to determine which of the reference images has the greatest correlation, wherein; the mean comprises an average value for a representative number of each reference image sub-region calculated as follows; N is a number of images of the training set, and the training set comprises ones of the reference images.
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11. A system for performing image recognition, the system comprising:
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an image input device which inputs a first image; a database having a set of reference images; and a correction unit which receives the first image having been divided into first image sub-regions, compares each of the first image sub-regions with a mean for the corresponding first image sub-region to respectively remove an influence of illumination and/or occlusion in each of the first image sub-regions to produce corrected first image sub-regions, and produces a corrected first image based on the corrected first image sub-regions. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18)
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19. A system for performing image recognition, the system comprising:
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an image input device which inputs a first image; a database having a set of reference images; and a correction unit which receives the first image having been divided into first image sub-regions, compares each of the first image sub-regions with a mean for the corresponding first image sub-region to remove an influence of illumination and/or occlusion in each of the first image sub-regions to produce corrected first image sub-regions, and produces a corrected first image based on the corrected first image sub-regions, wherein; the correction unit selects a Gamma parameter for each of the first image sub-regions by minimizing a distance between a pair wise kth first image sub-region and kth sub-region of a mean image as follows,
Ixyk′
=G(Ixyk;
γ
k*),computes γ
k* as follows,performs Gamma correction as follows; Ik is the kth first image sub-region of the first image, Iko is the kth sub-region of the mean image, I is the first image, Io is the mean image, and c is a constant. - View Dependent Claims (20)
andN is a number of images of the training set of reference images.
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21. A method of determining a correspondence between an obtained image divided into obtained image sub-regions and a set of reference images divided into corresponding reference image sub-regions, comprising:
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determining a greatest correlation between one of the obtained image sub-regions and corresponding sub-regions for the reference images through independent respective sub-region comparisons; determining another greatest correlation between another one of the obtained image sub-regions and corresponding sub-regions for the reference images through independent respective sub-region comparisons; and selecting one of the reference images based upon the one and another greatest correlations. - View Dependent Claims (22)
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23. At least one computer readable medium encoded with processing instructions for implementing a method of 21 performed by at least one computer.
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24. A method of removing an influence of illumination and/or occlusions of an obtained image, comprising:
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for each respective sub-region of an obtained image, determining a sub-region factor which minimizes a difference between the sub-region and a mean for the sub-region; and applying the sub-region factor to the corresponding sub-region for each of the sub-regions such that the entire obtained image is corrected. - View Dependent Claims (25, 26, 27, 28, 29, 30)
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31. At least one computer readable medium encoded with processing instructions for implementing a method of 24 performed by at least one computer.
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32. A system for performing image recognition, the system comprising:
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an image input device which inputs a first image; a database having a set of reference images; and a comparison unit which receives the first image having been divided into first image sub-regions, independently compares the first image sub-regions with corresponding reference image sub-regions of the reference images, respectively, and determines based on the comparison which of the reference images has a greatest correlation to the first image based on the comparisons between the reference image sub-regions and the first image sub-regions. - View Dependent Claims (33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55)
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Specification