Confidence based vein image recognition and authentication

A method of searching for a query object within an object class, said method comprising:

• (a) accessing a collection of unique training samples of multiple training objects within said object class;
• (b) defining a separate training set of training item descriptors from each of said training samples;
• (c) creating a composite collection of training item descriptors from the separate training sets of sample item descriptors;
• (d) creating a hierarchical tree from said composite collection of training item descriptors according to relations in the training item descriptors, said hierarchical tree having a plurality of leaf nodes;
• (e) accessing registration sets of registration item descriptors defined from respective registration samples obtained from registration objects of said object class, distributing said registration sets of registration item descriptors into said hierarchical tree according to said relations defined in the creation of said hierarchical tree, indexing the registration item descriptors clustered within each leaf node to their corresponding registration samples;
• (f) defining a separate registration model for each registration sample based on the clustering of its corresponding registration item descriptors in each leaf node;
• (g) accessing a query sample from said query object, defining a query set of query item descriptors from said query sample, distributing said query set of query item descriptors into said hierarchical tree according to said relations defined in the creation of said hierarchical tree;
• (h) defining a query model for said query sample based on the clustering of said query item descriptors in each leaf node;
• and
• (i) using the query model, x, and the registration models to identify as a potential candidate match the registration object, i, that renders the highest confidence i* of matching the query object, defined as i*=arg maxi P(x|i)P(i);
• wherein in step (i):
• each registration object i corresponds to Mi registration models;
• said query model and said registration models are in matrix form;
• the registration models are denoted μij where i specifies a registration model's corresponding registration object and j identifies a specific one of the multiple registration models 1−Mi that correspond to registration object i;
• the registration samples that corresponds to the same registration object are described by a Gaussian mixture model, as follows:
• P(x|i)˜{wij,N(x|μij,σij2)}∀j=1, . . . Mi
• where N is the normal distribution, σ is the variance, and wij=1/Mi;
• and
• the registration object i whose corresponding confidence i* satisfies the relationship i*=arg maxi=(maxj(xμijT)) is deemed said potential candidate match.

A method of searching for a query object within an object class, said method comprising:

• (a) accessing a collection of unique training samples of multiple training objects within said object class;
• (b) defining a separate training set of training item descriptors from each of said training samples;
• (c) creating a composite collection of training item descriptors from the separate training sets of sample item descriptors;
• (d) creating a hierarchical tree from said composite collection of training item descriptors according to relations in the training item descriptors, said hierarchical tree having a plurality of leaf nodes;
• (e) accessing registration sets of registration item descriptors defined from respective registration samples obtained from registration objects of said object class, distributing said registration sets of registration item descriptors into said hierarchical tree according to said relations defined in the creation of said hierarchical tree, indexing the registration item descriptors clustered within each leaf node to their corresponding registration samples;
• (f) defining a separate registration model for each registration sample based on the clustering of its corresponding registration item descriptors in each leaf node;
• (g) accessing a query sample from said query object, defining a query set of query item descriptors from said query sample, distributing said query set of query item descriptors into said hierarchical tree according to said relations defined in the creation of said hierarchical tree;
• (h) defining a query model for said query sample based on the clustering of said query item descriptors in each leaf node;
• and
• (i) using the query model, x, and the registration models to identify as a potential candidate match the registration object, i, that renders the highest confidence i* of matching the query object, defined as i*=arg maxi P(x|i)P(i);
• wherein for each registration object, a Gaussian mixture is defined for all its corresponding registration models, and an authentication confidence s is defined as
• s=maxi(maxj(xμijT))
• where μij denotes the registration models, i specifies a registration model's corresponding registration object, j identifies a specific one of the multiple registration models that correspond to registration object i, and T is the transform operator;
• and
• IF s is greater than a predefined threshold of confidence greater than i*, THEN said query object is deemed to be authenticated and specifically correspond to the potential candidate match.