Signature recognition system and method

A method of authenticating a signature, comprising the steps of:

• sampling a signature and storing data representative of said signature;
• converting said data to high dimension vectors using a recursive sampling process;
• feeding said high dimension vectors to an unsupervised neural network and performing a high order principal component extraction process on said high dimension vectors by cumulative orthonormalization, thereby identifying clusters of high dimension points;
• analyzing said clusters of high dimension points to determine, based on previously stored information, the authenticity of said signature;
• generating said information by:
• providing a plurality of sample signatures;
• effecting said sampling, converting and feeding steps for each of said signatures;
• computing a temporal summation r and an average temporal summation s for each of said signatures;
• and
• based on said computed r and s values, outputting a measure of global signature structure deviation A and a measure of local signature structure deviation B.

A method of authenticating a signature, comprising the steps of:

• sampling a signature and storing data representative of said signature;
• converting said data to high dimension vectors using a recursive sampling process;
• feeding said high dimension vectors to an unsupervised neural network and performing a high order principal component extraction process on said high dimension vectors by cumulative orthonormalization, thereby identifying clusters of high dimension points;
• analyzing said clusters of high dimension points to determine, based on previously stored information, the authenticity of said signature;
• and
• assessing the presence of overgeneralization in said neural network, wherein a condition of overgeneralization is deemed to occur when at least one of a ratio of the number of vectors within an ellipsoid to the total number of vectors squared (r2) and an average of distances of all vectors within said ellipsoid squared (S2) divided by the variance of the value r or s, respectively, is greater than a predetermined threshold.

Software stored in a computer storage medium for authenticating a signature, the software operable to:

• sample a signature and storing data representative of the signature;
• convert the data to high dimension vectors using a recursive sampling process, the recursive sampling process comprising iteratively focusing on increasingly smaller features of the signature, defined by shorter time spans;
• feed the high dimension vectors to an unsupervised neural network and perform a high order principal component extraction process on the high dimension vectors by cumulative orthonormalization, thereby identifying clusters of high dimension points;
• and
• analyze the clusters of high dimension points to determine, based on previously stored information, the authenticity of the signature.

A system for authenticating a signature comprising:

• at least one memory operable to store data representatives of a plurality signatures;
• and
• one or more processors, collectively operable to:
• sample a first signature and storing a data representative of the first signature in memory;
• convert the data to high dimension vectors using a recursive sampling process, the recursive sampling process comprising iteratively focusing on increasingly smaller features of said signature, defined by shorter time spans;
• feed the high dimension vectors to an unsupervised neural network and perform a high order principal component extraction process on the high dimension vectors by cumulative orthonormalization, thereby identifying clusters of high dimension points;
• and
• analyze the clusters of high dimension points to determine, based on previously stored information, the authenticity of the first signature.

A system for authenticating a signature, comprising:

• means for sampling a signature and storing data representative of the signature;
• means for converting the data to high dimension vectors using a recursive sampling process, the recursive sampling process comprises iteratively focusing on increasingly smaller features of the signature, defined by shorter time spans;
• means for feeding the high dimension vectors to an unsupervised neural network and performing a high order principal component extraction process on the high dimension vectors by cumulative orthonormalization, thereby identifying clusters of high dimension points;
• and
• means for analyzing the clusters of high dimension points to determine, based on previously stored information, the authenticity of the signature.