Signal processing
First Claim
1. A computer-implemented method for performing facial recognition, the method comprising:
- calculating, for one or more signals of a set of pre-classified signals, a parallelism value indicating a level of parallelism between that signal and an input signal, wherein each signal in the set of pre-classified signals is an image of a known person, and wherein the input signal is an image of a person to be identified;
calculating, for a first subset of the set of pre-classified signals, a sparse vector, wherein each element of the sparse vector serves as a coefficient for a corresponding signal of the first subset;
determining, for each of the signals in the set of pre-classified signals, a similarity value indicating a level of similarity between that signal and the input signal,wherein the determination of the similarity value for at least one of the signals in the first subset is based, at least in part, on the value of the corresponding element of the sparse vector, andwherein the determination of the similarity value for at least one of the one or more signals is based, at least in part, on the calculated parallelism value for that signal;
determining which signals in the first subset of signals correspond to elements in the sparse vector that have a positive value;
identifying a second subset of signals, the second subset of signals including the signals that correspond to elements of the sparse vector having a positive value,wherein the one or more signals for which parallelism values are calculated comprise all of the signals in the set of pre-classified signals that are not in the second subset, andwherein the determination of the similarity value for each of the signals that are not in the second subset is based, at least in part, on the calculated parallelism value for that signal; and
identifying the known person that corresponds to the input signal.
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Accused Products
Abstract
A computer-implemented method is provided for classifying an input signal against a set of pre-classified signals. A computer system may calculate, for each of one or more signals of the set of pre-classified signals, a parallelism value indicating a level of the parallelism between that signal and the input signal. The computer system may calculate, for a first subset of the set of pre-classified signals, a sparse vector, wherein each element of the sparse vector serves as a coefficient for a corresponding signal of the first subset. The computer system may determine, for each of the signals in the set of pre-classified signals, a similarity value indicating a level of similarity between that signal and the input signal.
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Citations
13 Claims
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1. A computer-implemented method for performing facial recognition, the method comprising:
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calculating, for one or more signals of a set of pre-classified signals, a parallelism value indicating a level of parallelism between that signal and an input signal, wherein each signal in the set of pre-classified signals is an image of a known person, and wherein the input signal is an image of a person to be identified; calculating, for a first subset of the set of pre-classified signals, a sparse vector, wherein each element of the sparse vector serves as a coefficient for a corresponding signal of the first subset; determining, for each of the signals in the set of pre-classified signals, a similarity value indicating a level of similarity between that signal and the input signal, wherein the determination of the similarity value for at least one of the signals in the first subset is based, at least in part, on the value of the corresponding element of the sparse vector, and wherein the determination of the similarity value for at least one of the one or more signals is based, at least in part, on the calculated parallelism value for that signal; determining which signals in the first subset of signals correspond to elements in the sparse vector that have a positive value; identifying a second subset of signals, the second subset of signals including the signals that correspond to elements of the sparse vector having a positive value, wherein the one or more signals for which parallelism values are calculated comprise all of the signals in the set of pre-classified signals that are not in the second subset, and wherein the determination of the similarity value for each of the signals that are not in the second subset is based, at least in part, on the calculated parallelism value for that signal; and identifying the known person that corresponds to the input signal. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A system for performing facial recognition, the system comprising a processor configured to:
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calculate, for each of one or more signals of a set of pre-classified signals, a parallelism value indicating a measure of parallelism between that signal and an input signal, wherein each signal in the set of pre-classified signals is an image of a known person, and wherein the input signal is an image of a person to be identified; calculate, for a first subset of the set of pre-classified signals, a sparse vector for use in a linear combination with the first subset of signals to provide a sparse approximation of the input signal, wherein each element of the sparse vector serves as a coefficient for a corresponding signal of the first subset in the linear combination; determine, for each of the signals in the set of pre-classified signals, a similarity value indicating a measure of similarity between that signal and the input signal, wherein the determination of the similarity value for at least one of the signals in the first subset is based, at least in part, on the value of the corresponding element of the sparse vector, and wherein the determination of the similarity value for at least one of the one or more signals is based, at least in part, on the calculated parallelism value for that signal; determine which signals in the first subset of signals correspond to elements in the sparse vector that have a positive value; identify a second subset of signals, the second subset of signals including the signals that correspond to elements of the sparse vector having a positive value, wherein the one or more signals for which parallelism values are calculated comprise all of the signals in the set of pre-classified signals that are not in the second subset, and wherein the determination of the similarity value for each of the signals that are not in the second subset is based, at least in part, on the calculated parallelism value for that signal; and identify the known person that corresponds to the input signal. - View Dependent Claims (10, 11)
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12. A computer program product for performing facial recognition, the computer program product comprising a computer readable storage medium having stored thereon:
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first program instructions executable by a processor to cause the processor to calculate, for each of one or more signals of a set of pre-classified signals, a parallelism value indicating a measure of parallelism between that signal and an input signal, wherein each signal in the set of pre-classified signals is an image of a known person, and wherein the input signal is an image of a person to be identified; second program instructions executable by the processor to calculate, for a first subset of the set of pre-classified signals, a sparse vector for use in a linear combination with the first subset of signals to provide a sparse approximation of the input signal, wherein each element of the sparse vector serves as a coefficient for a corresponding signal of the first subset in the linear combination; third program instructions executable by the processor to cause the processor to determine, for each of the signals in the set of pre-classified signals, a similarity value indicating a measure of similarity between that signal and the input signal, wherein the determination of the similarity value for at least one of the signals in the first subset is based, at least in part, on the value of the corresponding element of the sparse vector, and wherein the determination of the similarity value for at least one of the one or more signals is based, at least in part, on the calculated parallelism value for that signal; fifth program instructions executable by the processor to cause the processor to identify a second subset of signals from the first subset by identifying those signals which correspond to elements of the sparse vector having a positive value, wherein the one or more signals which the first program instructions cause the processor to calculate parallelism values for comprise all of the signals in the set of pre-classified signals that are not in the second subset, and wherein the third program instructions cause the processor to determine the similarity value for each of the signals that are not in the second subset is based, at least in part, on the calculated parallelism value for that signal; sixth program instructions executable by the processor to cause the processor to identify a minimum positive value of the elements of the sparse vector; seventh program instructions executable by the processor to cause the processor to scale the parallelism values for each of the pre-classified signals that are not in the second subset into a range between zero and the minimum positive value, wherein, the third program instructions cause the processor to determine the similarity value for each of the signals in the set of pre-classified signals that are not in the second subset based, at least in part, on the scaled parallelism value for that signal; and eighth program instructions executable by the processor to cause the processor to identify the known person that corresponds to the input signal. - View Dependent Claims (13)
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Specification