Hand-based biometric analysis

Abstract

Hand-based biometric analysis systems and techniques are described which provide robust hand-based identification and verification. An image of a hand is obtained, which is then segmented into a palm region and separate finger regions. Acquisition of the image is performed without requiring particular orientation or placement restrictions. Segmentation is performed without the use of reference points on the images. Each segment is analyzed by calculating a set of Zernike moment descriptors for the segment. The feature parameters thus obtained are then fused and compared to stored sets of descriptors in enrollment templates to arrive at an identity decision. By using Zernike moments, and through additional manipulation, the biometric analysis is invariant to rotation, scale, or translation or an in put image. Additionally, the analysis utilizes re-use of commonly-seen terms in Zernike calculations to achieve additional efficiencies over traditional Zernike moment calculation.

1 .- 2 . (canceled) 
     
     
         3 . A method of determining identity using a computing system that implements a biometric analysis tool, the method comprising:
 with the computing system that implements the biometric analysis tool, performing biometric analysis on an image of a hand to produce plural feature parameters without requiring a particular orientation of the hand during image capture and without requiring a particular orientation of the hand during the biometric analysis, wherein the biometric analysis includes:
 determining a silhouette representation from the image of the hand; and 
 using the silhouette representation to determine the plural feature parameters, each of the plural feature parameters indicating geometry of a different part among palm and plural fingers of the hand; and 
   making an identity decision based at least in part on the plural feature parameters and stored descriptors representing one or more previously-analyzed hand images.   
     
     
         4 . The method of  claim 3 , wherein a different weight is associated with each of the plural feature parameters, respectively. 
     
     
         5 . The method of  claim 3 , wherein the plural feature parameters resulting from the biometric analysis are invariant to rotation, scale and translation. 
     
     
         6 . The method of  claim 3 , wherein the image represents a back view of the hand, and wherein determining the silhouette representation comprises, for each pixel value of plural pixel values of the image:
 comparing the pixel value to a threshold;   if the pixel value satisfies the threshold, assigning the pixel value a first binary value; and   otherwise, assigning the pixel value a second binary value.   
     
     
         7 . The method of  claim 3 , wherein the biometric analysis is performed without reference to direct physical measurements of the image of the hand or the silhouette representation, and wherein the biometric analysis is performed without reference to landmark points within the image of the hand or the silhouette representation. 
     
     
         8 . The method of  claim 3 , wherein the biometric analysis further comprises segmenting the silhouette representation, including:
 filtering out the plural fingers from the silhouette representation to identify a segment image for the palm;   removing the palm from the silhouette representation to identify segment images for the plural fingers, respectively.   
     
     
         9 . The method of  claim 3 , wherein the identity decision is a verification decision, and wherein the making the identity decision comprises:
 selecting a particular descriptor among the stored descriptors; and   verifying identity using the plural feature parameters and the particular descriptor.   
     
     
         10 . The method of  claim 3 , wherein the identity decision is an identification decision, and wherein the making the identity decision comprises:
 identifying a matching descriptor among the stored descriptors using the plural feature parameters and each of the stored descriptors, respectively, until the matching descriptor is identified.   
     
     
         11 . The method of  claim 3 , wherein the making the identity decision uses a threshold, the method further comprising setting the threshold depending on security level. 
     
     
         12 . The method of  claim 3 , wherein the making the identity decision is further based at least in part on separate fingerprint data, other biometric data and/or other identity data. 
     
     
         13 . The method of  claim 3 , wherein the making the identity decision uses feature-level fusion and includes:
 combining the plural feature parameters into a feature vector, including using a different weight associated with each of the plural feature parameters as part of the combining the plural feature parameters into the feature vector; and   for a given stored descriptor of the stored descriptors, comparing the feature vector with a stored vector for the given stored descriptor, wherein the identity decision is based at least in part on the comparing the feature vector with the stored vector for the given stored descriptor.   
     
     
         14 . The method of  claim 3 , wherein the making the identity decision uses score-level fusion and includes, for a given stored descriptor of the stored descriptors:
 for each feature parameter of the plural feature parameters:
 comparing the feature parameter to a corresponding parameter for the given stored descriptor; and 
 calculating a score value for the feature parameter based at least in part on a different weight associated with the feature parameter and the comparing the feature parameter to the corresponding parameter for the given stored descriptor; 
   calculating an overall score value based at least in part on the score values for the respective feature parameters; and   comparing the overall score value to a threshold, wherein the identity decision is based at least in part on the comparing the overall score value to the threshold.   
     
     
         15 . The method of  claim 3 , wherein the making the identity decision uses score-level fusion and includes, for a given stored descriptor of the stored descriptors:
 for each feature parameter of the plural feature parameters, comparing the feature parameter to a corresponding parameter for the given stored descriptor; and   using support vector machine classification to map results of the comparing for the plural feature parameters, respectively, to the identity decision.   
     
     
         16 . The method of  claim 3 , wherein the making the identity decision uses decision-level fusion and includes, for a given stored descriptor of the stored descriptors:
 for each feature parameter of the plural feature parameters:
 comparing the feature parameter to a corresponding parameter for the given stored descriptor to produce a feature score value; and 
 determining whether the feature score value satisfies one or more thresholds; 
   wherein the identity decision is based at least in part on the determination of whether the feature score values for the plural feature parameters satisfy the one or more thresholds.   
     
     
         17 . The method of  claim 3 , further comprising adjusting a different weight associated with each of the plural feature parameters, a threshold and/or other setting of the biometric analysis tool based at least in part on the identity decision. 
     
     
         18 . A computing system that implements a biometric analysis tool, the computing system including:
 a processing unit; and   memory storing computer-executable instructions for causing the computing system to perform biometric analysis operations comprising:
 performing biometric analysis on an image of a hand to produce plural feature parameters without requiring a particular orientation of the hand during image capture and without requiring a particular orientation of the hand during the biometric analysis, wherein the biometric analysis includes:
 determining a silhouette representation from the image of the hand; and 
 using the silhouette representation to determine the plural feature parameters, each of the plural feature parameters indicating geometry of a different part among palm and plural fingers of the hand; and 
 
 making an identity decision based at least in part on the plural feature parameters and stored descriptors representing one or more previously-analyzed hand images. 
   
     
     
         19 . The computing system of  claim 18 , wherein the image of the hand is received from an image acquisition device that lacks pegs to guide the orientation of the hand. 
     
     
         20 . A memory device storing computer-executable instructions for causing a processor, when programmed thereby, to perform biometric analysis operations comprising:
 performing biometric analysis on an image of a hand to produce plural feature parameters without requiring a particular orientation of the hand during image capture and without requiring a particular orientation of the hand during the biometric analysis, wherein the biometric analysis includes:
 determining a silhouette representation from the image of the hand; and 
 using the silhouette representation to determine the plural feature parameters, each of the plural feature parameters indicating geometry of a different part among palm and plural fingers of the hand; and 
   making an identity decision based at least in part on the plural feature parameters and stored descriptors representing one or more previously-analyzed hand images.   
     
     
         21 . The memory device of  claim 20 , wherein a different weight is associated with each of the plural feature parameters, respectively. 
     
     
         22 . The memory device of  claim 20 , wherein the plural feature parameters resulting from the biometric analysis are invariant to rotation, scale and translation.