US6996257B2 — Method for lighting- and view -angle-invariant fa…

Claim 1

Independent Claim 40 words

A method of extracting features for a lighting-invariant face description, comprising: getting adjusted second-order eigenfeatures of a face image; quantizing the adjusted second-order eigenfeatures; and selecting features to construct a face descriptor to describe faces from the quantized second-order eigenfeatures.

Claim 2

Independent Claim 40 words

A method of extracting features for a lighting-invariant face description, comprising: getting adjusted second-order eigenfeatures of a face image; quantizing the adjusted second-order eigenfeatures; and selecting features to construct a face descriptor to describe faces from the quantized second-order eigenfeatures.

Claim 3

Independent Claim 49 words

A method of extracting features for a lighting-invariant face description, comprising: getting adjusted second-order eigenfeatures of a face image; quantizing the adjusted second-order eigenfeatures; selecting features to construct a face descriptor to describe faces from the quantized second-order eigenfeatures; and coding the selected eigenfeatures in a lighting-invariant face descriptor.

Claim 4

Independent Claim 49 words

A method of extracting features for a lighting-invariant face description, comprising: getting adjusted second-order eigenfeatures of a face image; quantizing the adjusted second-order eigenfeatures; selecting features to construct a face descriptor to describe faces from the quantized second-order eigenfeatures; and coding the selected eigenfeatures in a lighting-invariant face descriptor.

Claim 5

Independent Claim 55 words

A method of extracting features for a view-angle-invariant face description, comprising: getting adjusted first-order eigenfeatures of a face image; getting adjusted second-order eigenfeatures of a face image; quantizing the adjusted first-order eigenfeatures; quantizing the adjusted second-order eigenfeatures; and selecting features to construct a face descriptor to describe faces from the quantized first-order and second-order eigenfeatures.

Claim 6

Independent Claim 55 words

A method of extracting features for a view-angle-invariant face description, comprising: getting adjusted first-order eigenfeatures of a face image; getting adjusted second-order eigenfeatures of a face image; quantizing the adjusted first-order eigenfeatures; quantizing the adjusted second-order eigenfeatures; and selecting features to construct a face descriptor to describe faces from the quantized first-order and second-order eigenfeatures.

Claim 7

Independent Claim 55 words

A method of extracting features for a view-angle-invariant face description, comprising: getting adjusted first-order eigenfeatures; getting adjusted second-order eigenfeatures; quantizing the adjusted first-order eigenfeatures; quantizing the adjusted second-order eigenfeatures; selecting features to construct a face descriptor to describe faces from the quantized first-order and second-order eigenfeatures; and coding selected eigenfeatures in a view-angle-invariant face descriptor.

Claim 8

Independent Claim 55 words

A method of extracting features for a view-angle-invariant face description, comprising: getting adjusted first-order eigenfeatures; getting adjusted second-order eigenfeatures; quantizing the adjusted first-order eigenfeatures; quantizing the adjusted second-order eigenfeatures; selecting features to construct a face descriptor to describe faces from the quantized first-order and second-order eigenfeatures; and coding selected eigenfeatures in a view-angle-invariant face descriptor.

Claim 9

Dependent Claim 30 words

The method of claim 1 , wherein getting adjusted second-order eigenfeatures of a face image comprising: getting a dot product of the face image and an adjusted second-order eigenface matrix.

Claim 10

Dependent Claim 30 words

The method of claim 3 , wherein getting adjusted first-order eigenfeatures of a face image comprises: getting a dot product of the face image and an adjusted first-order eigenface matrix.

Claim 11

Dependent Claim 26 words

The method of claim 6 , further comprising computing an adjusted first-order eigenface matrix by: calculating a first-order eigenface matrix; and adjusting the first-order eigenface matrix.

Claim 12

Dependent Claim 26 words

The method of claim 5 , further comprising computing the adjusted second-order eigenface matrix by: calculating a second-order eigenface matrix; and adjusting the second-order eigenface matrix.

Claim 13

Dependent Claim 100 words

The method of claim 7 , wherein adjusting the first-order eigenfaces comprises: getting first-order eigenfeatures of training face images; arranging the first-order eigenface as a two dimensional array of original images; getting a mirrored eigenface of the two dimensional array; weighting the mirrored eigenface image; adding the weighted eigenface image to the first-order eigenface; re-arranging the first-order eigenface to obtain a one-dimensional adjusted first-order eigenface; normalizing adjusted first-order eigenface; getting weights of adjusted first-order eigenfeatures for a distance computation; multiplying the weights of the first-order eigenfeatures for distance computation to adjusted first-order eigenface matrix; and quantizing the first-order eigenface matrix.

Claim 14

Dependent Claim 76 words

The method of claim 8 , wherein calculating second-order eigenfaces comprises: calculating a first-order eigenface matrix; getting first-order eigenfeatures from training face images; calculating a pseudo-inverse of the first-order eigenface matrix; calculating first-order reconstructed face images by multiplying the first-order eigenfeatures to the pseudo-inverse of the first-order eigenface matrix; getting second-order residue images by subtracting the first-order reconstructed face images from original images; and getting the second-order eigenfaces by calculating eigenvectors of the second-order residue images.

Claim 15

Dependent Claim 104 words

The method of claim 8 , wherein adjusting second-order eigenfaces comprises: getting second-order eigenfeatures of training images by computing dot products of face images and second-order eigenfaces; re-shaping the second-order eigenfaces to an original image shape and getting left-right mirrored eigenface images; weighting the mirrored eigenface images and adding them to corresponding original image shaped second-order eigenface images; re-arranging original image shaped second-order eigenfaces to obtain one-dimensional adjusted second-order eigenfaces; normalizing adjusted second-order eigenfaces; getting weights of second-order eigenfeatures for a distance computation; multiplying the weights of second-order eigenfeatures for the distance computation to adjusted second-order eigenface matrix; and quantizing the second-order eigenface matrix.

Claim 16

Dependent Claim 59 words

The method of claim 9 , wherein quantizing the first-order eigenface matrix comprises: getting a maximum and a minimum of adjusted first-order eigenface matrix; getting a quantization by dividing an interval between the maximum and the minimum into a plurality of quantization levels; dividing the adjusted first-order eigenface matrix with the quantization; and rounding divided values to nearest integers.

Claim 17

Dependent Claim 60 words

The method of claim 11 , wherein quantizing the second-order eigenface matrix comprises: getting a maximum and a minimum of said adjusted second-order eigenface matrix; getting a quantization by dividing an interval between the maximum and the minimum into a plurality of quantization levels; dividing the adjusted second-order eigenface matrix with the quantization; and rounding divided values to nearest integers.

Claim 18

Dependent Claim 44 words

The method of claim 1 , wherein getting adjusted second-order eigenfeatures comprises: getting a recovered adjusted second-order eigenface by multiplying the quantized second-order eigenface matrix with a quantization; and getting second-order eigenfeatures by multiplying each column-wise recovered adjusted second-order eigenface with a row-wise image.

Claim 19

Dependent Claim 44 words

The method of claim 3 , wherein getting adjusted first-order eigenfeatures comprises: getting a recovered adjusted first-order eigenface by multiplying the quantized first-order eigenface matrix with a quantization; and getting first-order eigenfeatures by multiplying each column-wise recovered adjusted first-order eigenface with a row-wise image.

Claim 20

Dependent Claim 59 words

The method of claim 1 , wherein quantizing adjusted second-order eigenfeatures comprises: getting a maximum and a minimum of the adjusted second-order eigenfeatures of training images; getting the quantization by dividing an interval between the maximum and the minimum into a plurality of quantization levels; dividing adjusted second-order eigenfeatures with the quantization; and rounding divided values to nearest integers.

Claim 21

Dependent Claim 99 words

The method of claim 1 , wherein quantizing adjusted second-order eigenfeatures comprises: getting a maximum and a minimum of adjusted second-order eigenfeatures of training images; getting the quantization by dividing an interval between the maximum and the minimum into a plurality of quantization levels; dividing adjusted second-order eigenfeatures with the quantization; rounding divided values to nearest integers; allocating different number of bits to different eigenfeatures by computing a rounded logarithm of a standard deviation of corresponding eigenfeatures in a training set divided by a minimum of the standard deviations; and quantizing the eigenfeatures according to the corresponding bit allocation.

Claim 22

Dependent Claim 59 words

The method of claim 3 , wherein quantizing adjusted first-order eigenfeatures comprises: getting a maximum and a minimum of adjusted first-order eigenfeatures of training images; getting the quantization by dividing an interval between the maximum and the minimum into a plurality of quantization levels; dividing the adjusted first-order eigenfeatures with the quantization; and rounding divided values to nearest integers.

Claim 23

Dependent Claim 98 words

The method of claim 3 , wherein quantizing adjusted first-order eigenfeatures comprises: getting a maximum and a minimum of adjusted first-order eigenfeatures of training images; getting the quantization by dividing an interval between the maximum and the minimum into a plurality of quantization levels; dividing the adjusted first-order eigenfeatures with quantization; rounding divided values to nearest integers; allocating different number of bits to different eigenfeatures by computing a rounded logarithm of a standard deviation of corresponding eigenfeatures in a training set divided by a minimum of the standard deviations; and quantizing eigenfeatures according to the corresponding bit allocation.

Claim 24

Dependent Claim 39 words

The method of claim 9 , wherein getting weights of adjusted first-order eigenfeatures for a distance computation comprises: getting standard deviations of adjusted first-order eigenfeatures of training face images; and getting weights by extracting square roots of standard deviations.

Claim 25

Dependent Claim 42 words

The method of claim 11 , wherein getting weights of adjusted second-order eigenfeatures for a distance computation comprises: getting variances of adjusted second-order eigenfeatures of training face images; and getting weights of adjusted second-order eigenfeatures by extracting square roots of said variances.

Claim 26

Dependent Claim 47 words

A method of measuring lighting-invariant similarity between faces, comprising: extracting eigenfeatures of faces for a lighting-invariant face description with the method of claim 1 ; getting Euclidean distances of eigenfeatures of the faces; and choosing a smallest Euclidean distance to indicate a best matching pair of faces.

Claim 27

Dependent Claim 48 words

A method of measuring a view-angle-invariant similarity between faces, comprising: extracting eigenfeatures of faces for a view-angle-invariant face description with the method of claim 3 ; getting Euclidean distances of eigenfeatures of the faces; and choosing a smallest Euclidean distance to indicate a best matching pair of faces.

Claim 28

Dependent Claim 59 words

A method of getting code tables of variable length coding (VLC) for a light-invariant face descriptor, comprising: getting quantized eigenfeatures of a training set with the method of claim 1 ; classifying eigenfeatures into groups depending on bit allocations; and constructing a code table for each group of eigenfeatures with the same bit allocation, using an entropy coding method.

Claim 29

Dependent Claim 59 words

A method of getting code tables of variable length coding (VLC) for a view-angle-invariant face descriptor, comprising: getting quantized eigenfeatures of a training set with the method of claim 3 ; classifying eigenfeatures into groups depending on bit allocations; and constructing a code table for each group of eigenfeatures with the same bit allocation, using an entropy coding method.

Claim 30

Dependent Claim 24 words

The method of claim 24 , wherein the entropy coding method comprises a Huffman coding method based on a probability of a quantization level.

Claim 31

Dependent Claim 24 words

The method of claim 24 , wherein the entropy coding method comprises an Arithmetic coding method based on a probability of a quantization level.

Claim 32

Dependent Claim 35 words

The method of claim 1 , further comprising coding a lighting-invariant face descriptor by: looking up a code table generated for each quantized eigenfeature and using a corresponding code word to represent the quantized eigenfeature.

Claim 33

Dependent Claim 35 words

The method of claim 3 , further comprising coding a viewing-angle-invariant face descriptor by: looking up a code table generated for each quantized eigenfeature and using a corresponding code word to represent the quantized eigenfeature.

Claim 34

Dependent Claim 45 words

A method of extracting features for a general face description, comprising: getting adjusted first-order eigenfeatures of a face image with the method in claim 7 ; quantizing adjusted first-order eigenfeatures; and selecting features to construct face descriptor to describe faces from the quantized first-order eigenfeatures.

Claim 35

Dependent Claim 51 words

A method of extracting features for general face description, comprising: getting adjusted first-order eigenfeatures of a face image with the method in claim 7 ; quantizing adjusted first-order eigenfeatures; selecting features to construct face descriptor to describe faces from the quantized first-order eigenfeatures; and coding selected eigenfeatures in the face descriptor.

Claim 36

Dependent Claim 38 words

The method of claim 30 , wherein selecting features to construct a face descriptor to describe faces from the quantized first-order eigenfeatures comprises: selecting eigenfeatures corresponding to a top N largest eigenvalues for a view-angle invariant face description.

Claim 37

Dependent Claim 39 words

The method of claim 30 , wherein selecting features to construct a face descriptor to describe faces from the quantized first-order eigenfeatures comprises: selecting eigenfeatures corresponding to a k-th to N-th largest eigenvalues (0<k<N) for a lighting-invariant face description.

Claim 38

Independent Claim 155 words

A method of lighting-invariant a view-angle-invariant face description, comprising: getting first-order residual image data Γ (1) , by taking face image data Φ as a vector and calculating its difference from a mean face image data Ψ; multiplying the first-order residual image data Γ (1) and a combination of a first-order eigenmatrix obtained by decomposing the first-order residual image data Γ (1) , and a second-order eigenmatrix obtained by decomposing second-order residual image data Γ (2) obtained by subtracting first-order reconstructed image data from original face image data Φ, where the first-order reconstructed image data is obtained by adding mean face image data Ψ and a substantial low-frequency component extracted from the first-order residual image data Γ (1) , taking a result of the multiplication as an eigenfeature of the face image data Φ; quantizing the eigenfeature; encoding the quantized eigenfeature into variable length codes; and taking a result of encoding as the face descriptor.

Claim 39

Independent Claim 155 words

A method of lighting-invariant a view-angle-invariant face description, comprising: getting first-order residual image data Γ (1) , by taking face image data Φ as a vector and calculating its difference from a mean face image data Ψ; multiplying the first-order residual image data Γ (1) and a combination of a first-order eigenmatrix obtained by decomposing the first-order residual image data Γ (1) , and a second-order eigenmatrix obtained by decomposing second-order residual image data Γ (2) obtained by subtracting first-order reconstructed image data from original face image data Φ, where the first-order reconstructed image data is obtained by adding mean face image data Ψ and a substantial low-frequency component extracted from the first-order residual image data Γ (1) , taking a result of the multiplication as an eigenfeature of the face image data Φ; quantizing the eigenfeature; encoding the quantized eigenfeature into variable length codes; and taking a result of encoding as the face descriptor.

Annuity Stage	Window Opens	Payment Deadline	Surcharge Period Until	Estimated Fee	Status
3.5 Years (Year 4)	2009-02-06	2009-08-08	2010-02-07	$2000.00	Window Closed / Lapsed
7.5 Years (Year 8)	2013-02-06	2013-08-08	2014-02-07	$3760.00	Window Closed / Lapsed
11.5 Years (Year 12)	2017-02-06	2017-08-08	2018-02-07	$7700.00	Window Closed / Lapsed

Method for lighting- and view -angle-invariant face description with first- and second-order eigenfeatures

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