System and method of face recognition using proportions of learned model

Claim 1

A method for classifying facial image data, the method comprising the steps of: a) training a neural network classifier device for recognizing one or more facial images and obtaining corresponding learned models of the facial images used for training; b) inputting a vector including data representing a portion of an unknown facial image to be recognized into said classifier; c) classifying said portion of said unknown facial image according to a classification method at each iteration, comparing a portion of the unknown image against a corresponding portion of the learned model image for each class, and obtaining a confidence score for each classified portion; d) repeating step b) and c) using a different portion of said unknown facial image at each iteration; and, e) identifying a single class result from said different portions input to said classifier, applying a rule to said confidence scores to obtain said single class result, said confidence score is a probability measure that a current portion of an unknown facial image is identified with a class, said applied rule including obtaining class having majority of class labels determined for each unknown facial image.

Claim 2

A method for classifying facial image data, the method comprising the steps of: a) training a neural network classifier device for recognizing one or more facial images and obtaining corresponding learned models of the facial images used for training; b) inputting a vector including data representing a portion of an unknown facial image to be recognized into said classifier; c) classifying said portion of said unknown facial image according to a classification method at each iteration, comparing a portion of the unknown image against a corresponding portion of the learned model image for each class, and obtaining a confidence score for each classified portion; d) repeating step b) and c) using a different portion of said unknown facial image at each iteration; and, e) identifying a single class result from said different portions input to said classifier, applying a rule to said confidence scores to obtain said single class result, said confidence score is a probability measure that a current portion of an unknown facial image is identified with a class, said applied rule including obtaining class having majority of class labels determined for each unknown facial image.

Claim 7

A method for classifying facial image data, the method comprising the steps of: a) training a neural network classifier device for recognizing one or more facial images and obtaining corresponding learned models of the facial images used for training, wherein a Radial Basis Function Network is implemented for training and classifying each image portion, said training step comprises: i) initiating the Radial Basis Function Network, the initializing step comprising the steps of: fixing the network structure by selecting a number of basis functions F, where each basis function I has the output of a Gaussian non-linearity, determining the basis function means μ I where I=1, . . . , F, using a K-means clustering algorithm, determining the basis function variances σ I 2 , and determining a global proportionality factor H, for the basis function variances by empirical search; ii) presenting the training, the presenting step comprising the steps of: inputting training patterns X(p) and their class labels C(p) to the classification method, where the pattern index is p=1, . . . , N, computing the output of the basis function nodes y I (p), F, resulting from pattern X(p); computing the F×F correlation matrix R of the basis function outputs; and computing the F×M output matrix B, where d j is the desired output and M is the number of output classes and j=1, . . . , M, and iii) determining weights, the determining step comprising the steps of: inverting the F×F correlation matrix R to get R − ; and solving for the weights in the network; b) inputting a vector including data representing a portion of an unknown facial image to be recognized into said classifier; c) classifying said portion of said unknown facial image according to a classification method; d) repeating step b) and c) using a different portion of said unknown facial image at each iteration; and, e) identifying a single class result from said different portions input to said classifier.

Claim 8

A method for classifying facial image data, the method comprising the steps of: a) training a neural network classifier device for recognizing one or more facial images and obtaining corresponding learned models of the facial images used for training, wherein a Radial Basis Function Network is implemented for training and classifying each image portion, said training step comprises: i) initiating the Radial Basis Function Network, the initializing step comprising the steps of: fixing the network structure by selecting a number of basis functions F, where each basis function I has the output of a Gaussian non-linearity, determining the basis function means μ I where I=1, . . . , F, using a K-means clustering algorithm, determining the basis function variances σ I 2 , and determining a global proportionality factor H, for the basis function variances by empirical search; ii) presenting the training, the presenting step comprising the steps of: inputting training patterns X(p) and their class labels C(p) to the classification method, where the pattern index is p=1, . . . , N, computing the output of the basis function nodes y I (p), F, resulting from pattern X(p); computing the F×F correlation matrix R of the basis function outputs; and computing the F×M output matrix B, where d j is the desired output and M is the number of output classes and j=1, . . . , M, and iii) determining weights, the determining step comprising the steps of: inverting the F×F correlation matrix R to get R − ; and solving for the weights in the network; b) inputting a vector including data representing a portion of an unknown facial image to be recognized into said classifier; c) classifying said portion of said unknown facial image according to a classification method; d) repeating step b) and c) using a different portion of said unknown facial image at each iteration; and, e) identifying a single class result from said different portions input to said classifier.

Claim 9

A method for classifying facial image data, the method comprising the steps of: a) training a neural network classifier device for recognizing one or more facial images and obtaining corresponding learned models of the facial images used for training, wherein a Radial Basis Function Network is implemented for training and classifying each image portion, wherein said training step comprises: i) initiating the Radial Basis Function Network, the initializing step comprising the steps of: fixing the network structure by selecting a number of basis functions F, where each basis function I has the output of a Gaussian non-linearity; determining the basis function means μ I where I=1, . . . , F, using a K-means clustering algorithm; determining the basis function variances σ I 2 ; and determining a global proportionality factor H, for the basis function variances by empirical search; ii) presenting the training, the presenting step comprising the steps of: inputting training patterns X(p) and their class labels C(p) to the classification method, where the pattern index is p=1, . . . , N, computing the output of the basis function nodes y I (p), F, resulting from pattern X(p), computing the F×F correlation matrix R of the basis function outputs, and computing the F×M output matrix B, where d j is the desired output and M is the number of output classes and j=1, . . . , M and iii) determining weights, the determining step comprising the steps of: inverting the F×F correlation matrix R to get R −1 ; and solving for the weights in the network; b) inputting a vector including data representing a portion of an unknown facial image to be recognized into said classifier; c) classifying said portion of said unknown facial image according to a classification method, the classifying step further comprising: presenting each X test portion at each iteration to the classification method and classifying each X test by computing the basis function outputs, for all F basis functions, computing output node activations, and selecting the output Z j with the largest value and classifying the X test portion as a class j; d) repeating step b) and c) using a different portion of said unknown facial image at each iteration; and, e) identifying a single class result from said different portions input to said classifier.

Claim 10

A method for classifying facial image data, the method comprising the steps of: a) training a neural network classifier device for recognizing one or more facial images and obtaining corresponding learned models of the facial images used for training, wherein a Radial Basis Function Network is implemented for training and classifying each image portion, wherein said training step comprises: i) initiating the Radial Basis Function Network, the initializing step comprising the steps of: fixing the network structure by selecting a number of basis functions F, where each basis function I has the output of a Gaussian non-linearity; determining the basis function means μ I where I=1, . . . , F, using a K-means clustering algorithm; determining the basis function variances σ I 2 ; and determining a global proportionality factor H, for the basis function variances by empirical search; ii) presenting the training, the presenting step comprising the steps of: inputting training patterns X(p) and their class labels C(p) to the classification method, where the pattern index is p=1, . . . , N, computing the output of the basis function nodes y I (p), F, resulting from pattern X(p), computing the F×F correlation matrix R of the basis function outputs, and computing the F×M output matrix B, where d j is the desired output and M is the number of output classes and j=1, . . . , M and iii) determining weights, the determining step comprising the steps of: inverting the F×F correlation matrix R to get R −1 ; and solving for the weights in the network; b) inputting a vector including data representing a portion of an unknown facial image to be recognized into said classifier; c) classifying said portion of said unknown facial image according to a classification method, the classifying step further comprising: presenting each X test portion at each iteration to the classification method and classifying each X test by computing the basis function outputs, for all F basis functions, computing output node activations, and selecting the output Z j with the largest value and classifying the X test portion as a class j; d) repeating step b) and c) using a different portion of said unknown facial image at each iteration; and, e) identifying a single class result from said different portions input to said classifier.

Claim 11

An apparatus for classifying facial image data comprising: a neural network classifier device trained for recognizing one or more facial images and generating corresponding learned models associated with the facial images used for training; means for iteratively inputting a vector each including data representing a portion of an unknown facial image to be recognized into said classifier, a different image portion being input to said classifier at each iteration, said classifier device classifying each said portion of said unknown facial image according to a classification method; means for identifying a single class result from said different portions input to said classifier.

Claim 12

An apparatus for classifying facial image data comprising: a neural network classifier device trained for recognizing one or more facial images and generating corresponding learned models associated with the facial images used for training; means for iteratively inputting a vector each including data representing a portion of an unknown facial image to be recognized into said classifier, a different image portion being input to said classifier at each iteration, said classifier device classifying each said portion of said unknown facial image according to a classification method; means for identifying a single class result from said different portions input to said classifier.

Claim 18

An apparatus for classifying facial image data comprising: a neural network classifier device trained for recognizing one or ore facial images and generating corresponding learned models associated with the facial images used for training; means for iteratively inputting a vector each including data representing a portion of an unknown facial image to be recognized into said classifier, a different image portion being input to said classifier at each iteration, said classifier device classifying each said portion of said unknown facial image according to a classification method, said classifier includes a mechanism for comparing a portion of the unknown image against a corresponding portion of the learned model image for each class, at each iteration and obtaining a confidence score for each classified portion, said confidence score is a probability measure that a current portion of an unknown facial image is identified with a class, said applied rule including identifying class having majority of class labels determined for each unknown facial image; and means for identifying a single class result from said different portions input to said classifier, said means for identifying applies a rule to said confidence scores to obtain said single class result.

Claim 19

An apparatus for classifying facial image data comprising: a neural network classifier device trained for recognizing one or ore facial images and generating corresponding learned models associated with the facial images used for training; means for iteratively inputting a vector each including data representing a portion of an unknown facial image to be recognized into said classifier, a different image portion being input to said classifier at each iteration, said classifier device classifying each said portion of said unknown facial image according to a classification method, said classifier includes a mechanism for comparing a portion of the unknown image against a corresponding portion of the learned model image for each class, at each iteration and obtaining a confidence score for each classified portion, said confidence score is a probability measure that a current portion of an unknown facial image is identified with a class, said applied rule including identifying class having majority of class labels determined for each unknown facial image; and means for identifying a single class result from said different portions input to said classifier, said means for identifying applies a rule to said confidence scores to obtain said single class result.

Claim 22

A method for classifying facial image data, the method comprising: training a classifier device for recognizing one or more facial images and obtaining corresponding learned models the facial images used for training; inputting a vector including data representing a portion of an unknown facial image to be recognized into said classifier; classifying said portion of said unknown facial image according to a classification method; repeating the inputting and classifying using a different portion of said unknown facial image at each iteration; and, identifying a single class result from said different portions input to said classifier; and wherein: the classifying includes: at each iteration, comparing a portion of the unknown image against a corresponding portion of the learned model image for each class; and obtaining a confidence score for each classified portion, the confidence score being a probability measure that a current portion of an unknown facial image is identified with a class, an applied rule including obtaining class having majority of class labels determined for each unknown facial image; and the identifying includes applying the rule to said confidence scores to obtain said single class result.

Claim 23

A method for classifying facial image data, the method comprising: training a classifier device for recognizing one or more facial images and obtaining corresponding learned models the facial images used for training; inputting a vector including data representing a portion of an unknown facial image to be recognized into said classifier; classifying said portion of said unknown facial image according to a classification method; repeating the inputting and classifying using a different portion of said unknown facial image at each iteration; and, identifying a single class result from said different portions input to said classifier; and wherein: the classifying includes: at each iteration, comparing a portion of the unknown image against a corresponding portion of the learned model image for each class; and obtaining a confidence score for each classified portion, the confidence score being a probability measure that a current portion of an unknown facial image is identified with a class, an applied rule including obtaining class having majority of class labels determined for each unknown facial image; and the identifying includes applying the rule to said confidence scores to obtain said single class result.

Claim 24

A method for classifying facial image data, the method comprising: training a classifier device for recognizing one or more facial images and obtaining corresponding learned models the facial images used for training; inputting a vector including data representing a portion of an unknown facial image to be recognized into the classifier; classifying the portion of the unknown facial image according to a classification method; repeating the inputting and classifying using a different portion of the unknown facial image at each iteration; and, identifying a single class result from the different portions input to the classifier; and wherein: a Radial Basis Function Network is implemented for training and classifying each image portion; and the training includes: initiating the Radial Basis Function Network, the initializing including: fixing the network structure by selecting a number of basis functions F, where each basis function I has the output of a Gaussian non-linearity; determining the basis function means μ I where I=1, . . . , F, using a K-means clustering algorithm; determining the basis function variances σ I 2 ; and determining a global proportionality factor H, for the basis function variances by empirical search; presenting the training, the presenting including: inputting training patterns X(p) and their class labels C(p) to the classification method, where the pattern index is p=1, . . . , N; computing the output of the basis function nodes Y I (p), F, resulting from pattern X(p); computing the F×F correlation matrix R of the basis function outputs; and computing the F×M output matrix B, where d j is the desired output and M is the number of output classes and j=1, . . . , M; and determining weights, the determining including: inverting the F×F correlation matrix R to get R −1 ; and solving for the weights in the network.

Claim 25

A method for classifying facial image data, the method comprising: training a classifier device for recognizing one or more facial images and obtaining corresponding learned models the facial images used for training; inputting a vector including data representing a portion of an unknown facial image to be recognized into the classifier; classifying the portion of the unknown facial image according to a classification method; repeating the inputting and classifying using a different portion of the unknown facial image at each iteration; and, identifying a single class result from the different portions input to the classifier; and wherein: a Radial Basis Function Network is implemented for training and classifying each image portion; and the training includes: initiating the Radial Basis Function Network, the initializing including: fixing the network structure by selecting a number of basis functions F, where each basis function I has the output of a Gaussian non-linearity; determining the basis function means μ I where I=1, . . . , F, using a K-means clustering algorithm; determining the basis function variances σ I 2 ; and determining a global proportionality factor H, for the basis function variances by empirical search; presenting the training, the presenting including: inputting training patterns X(p) and their class labels C(p) to the classification method, where the pattern index is p=1, . . . , N; computing the output of the basis function nodes Y I (p), F, resulting from pattern X(p); computing the F×F correlation matrix R of the basis function outputs; and computing the F×M output matrix B, where d j is the desired output and M is the number of output classes and j=1, . . . , M; and determining weights, the determining including: inverting the F×F correlation matrix R to get R −1 ; and solving for the weights in the network.