US 7,492,943 B2Granted

Open set recognition using transduction

AssigneeGeorge Mason Intellectual Properties, Inc.

InventorsFayin Li, Harry Wechsler

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Abstract

An open set recognition system utilizing transductive inference including capture device(s), a basis, quality checker(s), feature extractor(s), a gallery, a rejection threshold, a storage mechanism, and a recognition stage. The basis encodes sample(s) and is derived using training samples. The feature extractor(s) generates signature(s) from sample(s) using the basis. The rejection threshold is created using a rejection threshold learning mechanism that calculates the rejection threshold using sample(s) by: swapping a sample identifier with other sample identifier(s); computing a credibility value for the swapped sample identifiers; deriving a peak-to-side ratio distribution using the credibility values; and determining the rejection threshold using the peak-to-side ratio distribution. The open set recognition stage authenticates or reject as unknown the identity of unknown sample(s) using derived credibility values, derived peak-to-side ratios for the unknown sample and the rejection threshold.

Claims21

Dependent claims

A pattern recognition system comprising:

a) at least one capture device configured to acquire at least one sample, each of said at least one sample associated with a sample identifier;
b) a basis configured to encode at least one of said at least one sample, said basis derived using a multitude of representative training samples;
c) at least one feature extractor configured to generate at least one signature from at least one of said at least one sample using said basis;
d) a gallery, said gallery including at least one gallery sample, each of said at least one gallery sample being one of said at least one signature;
e) a rejection threshold, said rejection threshold created using a rejection threshold learning mechanism, said rejection threshold learning mechanism configured to calculate said rejection threshold using at least one of said at least one sample by:
i) swapping one of said sample identifier with other possible said sample identifier;
ii) computing a credibility value (p) for each of the swapped sample identifiers;
iii) deriving a peak-to-side ratio (PSR) distribution using a multitude of said credibility value;
and
iv) determining said rejection threshold using said peak-to-side ratio distribution;
f) a storage mechanism configured to store at least one of said at least one gallery sample;
and
g) an open set recognition stage configured to authenticate or reject as unknown the identity of at least one unknown sample, by:
i) deriving a set of credibility values by iteratively assign each of the gallery identifiers to the unknown sample and calculating a credibility value;
ii) deriving a peak-to-side ratio for said unknown sample using said set of credibility values;
iii) comparing said peak-to-side ratio for said unknown sample to said rejection threshold;
iv) rejecting said unknown sample as unknown if said peak-to-side ratio is less than or equal to said rejection threshold;
and
v) finding the closest of said at least one gallery sample if said peak-to-side ratio is greater than said rejection threshold.

16 more elements

A pattern recognition method comprising using a system to perform the steps of:

a) acquiring at least one sample acquired from at least one capture device, each of said at least one sample associated with a sample identifier;
b) encoding at least one of said at least one sample using a basis, said basis derived using a multitude of representative training samples;
c)generating at least one signature from at least one of said at least one sample using said basis;
d) storing at least one of said at least one signature in a gallery, each of said at least one signature being a gallery sample;
e) calculating a rejection threshold using at least one of said at least one sample by:
i) swapping one of said sample identifier with other possible said sample identifier;
ii) computing a credibility value (p) for each of the swapped sample identifiers;
iii) deriving a peak-to-side ratio (PSR) distribution using a multitude of said credibility value;
and
iv) determining said rejection threshold using said peak-to-side ratio distribution;
and
f) authenticating or rejecting as unknown the identity of at least one unknown sample, by:
i) deriving a set of credibility values by iteratively assign each of the gallery identifiers to the unknown sample and calculating a credibility value;
ii) deriving a peak-to-side ratio for said unknown sample using said set of credibility values;
iii) comparing said peak-to-side ratio for said unknown sample to said rejection threshold;
iv) rejecting said unknown sample as unknown if said peak-to-side ratio is less than or equal to said rejection threshold;
and
v) finding the closest of said at least one gallery sample if said peak-to-side ratio is greater than said rejection threshold.

15 more elements

Dependencies are inferred from the claim text.

Facts

Application: 11/075,982Filed 10 Mar 2005 · us provisional application
Priority: 29 Oct 2004
Published: US 2006/0093208 A14 May 2006
Granted: 17 Feb 2009
Term ends: Not estimatedThe source records don’t support an estimate.

Classification7 codes

G06V 10/764
Using pattern recognition or machine learning: using classification, e.g. of video objects
G06F 18/24147
Based on distances to training or reference patterns: distances to closest patterns, e.g. nearest neighbour classification
G06V 40/10
Recognition of biometric, human-related or animal-related patterns in image or video data: human or animal bodies, e.g. vehicle occupants or…
G06V 40/172
Human faces, e.g. facial parts, sketches or expressions: classification, e.g. identification