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The present invention provides a biometrics-based cryptographic key generation system and method. A user-dependent distinguishable feature transform unit provides a feature transformation for each authentic user, which receives N-dimensional biometric features and performs a feature transformation t

The present invention provides a biometrics-based cryptographic key generation system and method. A user-dependent distinguishable feature transform unit provides a feature transformation for each authentic user, which receives N-dimensional biometric features and performs a feature transformation to produce M-dimensional feature signals, such that the transformed feature signals of the authentic user are compact in the transformed feature space while those of other users presumed as imposters are either diverse or far away from those of the authentic user. A stable key generation unit receives the transformed feature signals to produce a cryptographic key based on bit information respectively provided by the M-dimensional feature signals, wherein the length of the bit information provided by the feature signal of each dimension is proportional to the degree of distinguishability in the dimension.

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What is claimed is: 1. A biometrics-based cryptographic key generation system including a cryptographic key generation mechanism implemented on a computer by a set of instructions stored in a non-transitory computer-readable medium, said cryptographic key generation mechanism comprising: a user-dep

What is claimed is: 1. A biometrics-based cryptographic key generation system including a cryptographic key generation mechanism implemented on a computer by a set of instructions stored in a non-transitory computer-readable medium, said cryptographic key generation mechanism comprising: a user-dependent distinguishable feature transform unit included in said key generation mechanism for providing each authentic user a specific feature transformation trained with biometric features from both authentic user and non-authentic users, and receiving N-dimensional biometric features for performing the feature transformation to produce M-dimensional feature signals according to a cascaded linear discriminant analysis (CLDA) obtained by repeating a linear discriminant analysis multiple times, such that the transformed feature signals of the authentic user are compact in a transformed feature space while the transformed feature signals of non-authentic users presumed as imposters are diverse and far from the transformed feature signals of the authentic user; and a stable key generation unit included in said key generation mechanism for receiving the transformed feature signals to produce a cryptographic key based on bit information respectively provided by the M-dimensional feature signals, wherein a length of the bit information provided by the feature signal of each dimension is proportional to a degree of distinguishability determined by a compactness of a feature distribution of the authentic user with respect to a diversity of global feature distribution of all users in a corresponding dimension. 2. The system as claimed in claim 1, wherein the stable key generation unit produces the cryptographic key by cascading the bit information provided by the M-dimensional feature signals. 3. The system as claimed in claim 1, wherein the user-dependent distinguishable feature transform unit transforms the N-dimensional biometric features to M-dimensional feature signals according to a generalized symmetric max minimal distance in subspace (GSMMS) criterion. 4. A biometrics-based cryptographic key generation method implemented on a computer that includes a key generation mechanism, said method comprising: a user-dependent distinguishable feature transform step implemented by a user-dependent distinguishable feature transform unit of the key generation mechanism for providing each authentic user a specific feature transformation trained with biometric features from both the authentic user and non-authentic users, and receiving N-dimensional biometric features for performing the feature transformation to produce M-dimensional feature signals according to a cascaded linear discriminant analysis (CLDA) obtained by repeating a linear discriminant analysis multiple times, such that the transformed feature signals of the authentic user are compact in a transformed feature space while the transformed feature signals of non-authentic users presumed as imposters are diverse and far from the transformed feature signals of the authentic user; and a stable key generation step implemented by a stable key generation unit of the key generation mechanism for receiving the transformed feature signals to produce a cryptographic key based on bit information respectively provided by the M-dimensional feature signals, wherein a length of the bit information provided by the feature signal of each dimension is proportional to a degree of distinguishability determined by a compactness of a feature distribution of the authentic user with respect to a diversity of global feature distribution of all users in a corresponding dimension. 5. The method as claimed in claim 4, wherein the user-dependent distinguishable feature transform step transforms the N-dimensional biometric features to M-dimensional feature signals according to a generalized symmetric max minimal distance in subspace (GSMMS) criterion. 6. A biometrics-based cryptographic key generation method implemented on computer including a key generation mechanism, said method comprising: a user-dependent distinguishable feature transform step implemented by a user-dependent distinguishable feature transform unit of the key generation mechanism for providing each authentic user a specific feature transformation trained with biometric features from both the authentic user and non-authentic users, and receiving N-dimensional biometric features for performing the feature transformation to produce M-dimensional feature signals, such that the transformed feature signals of the authentic user are compact in a transformed feature space while the transformed feature signals of imposters are diverse and far from the transformed feature signals of the authentic user; and a stable key generation step implemented by a stable key generation unit of the key generation mechanism for receiving the transformed feature signals to produce a cryptographic key based on bit information respectively provided by the M-dimensional feature signals, the stable key generation step including: a setting step for setting a left boundary and a right boundary of a feature signal distribution in each dimension, and defining an authentic region of an authentic feature signal distribution, a distinguishing step for dividing at least one segment between the left boundary and the right boundary in a corresponding dimension according to an authentic region, where a length of each segment is proportional to the authentic region, an index specifying step for specifying the segment with an index thereby obtaining authentic bit information provided by the feature signal in a corresponding dimension, and a cascading step for cascading the bit information provided by the feature signal in each corresponding dimension for obtaining a cryptographic key, wherein a length of the bit information provided by the feature signal of each dimension is proportional to a degree of distinguishability in the corresponding dimension and the setting step sets the left boundary of the feature signal distribution in each dimension as LB=min(mg−kgσg,ma−kaσa,), and sets the right boundary as RB=max(mg+kgσg,ma+kaσa,), where ma and σa are mean and standard deviation of an authentic feature signal distribution in the corresponding dimension, mg and σg are mean and standard deviation of a global feature signal distribution in the corresponding dimension, ka and kg are parameters, and (ma−kaσa,ma+kaσa) is specified as the authentic region. 7. The method as claimed in claim 6, wherein kg is a fixed value parameter. 8. The method as claimed in claim 6, wherein ka is a fixed value parameter for each dimension. 9. The method as claimed in claim 6, wherein ka is defined as different parameters for different dimensions. 10. The method as claimed in claim 9, wherein ka is defined according to a receiver operation characteristic (ROC) curve optimization. 11. The method as claimed in claim 6, wherein in the distinguishing step, LS=┌(ma−kaσa−LB)/(2 kaσa)┐ segments of the same size as the authentic region are divided between the left boundary of the feature signal distribution and the left boundary (ma−kaσa) of the authentic region, and RS=┌(RB−ma−kaσa)/(2kaσa)┐ segments of the same size as the authentic region are divided between the right boundary (ma+kaσa) of the authentic region and the right boundary of the feature signal distribution, such that (LS+RS+1) segments are divided between the left boundary and the right boundary of the feature signal distribution. 12. The method as claimed in claim 11, wherein the index specifying step assigns each segment with the index according to ┌log2 (LS+RS+1)┐ bits. 13. A biometrics-based cryptographic key generation system including a cryptographic key generation mechanism implemented on a computer by a set of instructions stored in a non-transitory computer-readable medium, said cryptographic key generation mechanism, comprising: a user-dependent distinguishable feature transform unit implemented in said key generation mechanism for providing each authentic user a specific feature transformation trained with biometric features from both authentic user and non-authentic users, and receiving N-dimensional biometric features for performing the feature transformation to produce M-dimensional feature signals, such that the transformed feature signals of the authentic user are compact in a transformed feature space while the transformed feature signals of non-authentic users presumed as imposters are diverse and far from the transformed feature signals of the authentic user; and a stable key generation unit implemented in said key generation mechanism for receiving the transformed feature signals to produce a cryptographic key based on bit information respectively provided by the M-dimensional feature signals, wherein a length of the bit information provided by the feature signal of each dimension is proportional to a degree of distinguishability determined by a compactness of a feature distribution of the authentic user with respect to a diversity of global feature distribution of all users in a corresponding dimension, wherein the stable key generation unit divides at least one segment between a left boundary and a right boundary of a feature space in each dimension according to the authentic region of the authentic feature signal distribution, and specifies the segment with an index, thereby obtaining the authentic bit information provided by the feature signal in the corresponding dimension, where the left boundary of is defined as LB=min(mg−kgσg,ma−kaσa), the right boundary is defined as RB=max(mg+kgσg, ma+kaσa), and the authentic region is defined as (ma−kaσa, ma+kaσa), for ma and σaare the mean and standard deviation of the authentic feature signal distribution in the corresponding dimension, mg and σg are the mean and standard deviation of a global feature signal distribution in the corresponding dimension, and ka and kg are parameters. 14. A biometrics-based cryptographic key generation method implemented on a computer device having a key generating mechanism, comprising: a user-dependent distinguishable feature transform step implemented by a user-dependent distinguishable feature transform unit of the key generation mechanism for providing each authentic user a specific feature transformation trained with biometric features from both the authentic user and non-authentic users, and receiving N-dimensional biometric features for performing the feature transformation to produce M-dimensional feature signals, such that the transformed feature signals of the authentic user are compact in a transformed feature space while the transformed feature signals of non-authentic users presumed as imposters are diverse and far from the transformed feature signals of the authentic user; and a stable key generation step implemented by a stable key generation unit of the key generation mechanism for receiving the transformed feature signals to produce a cryptographic key based on bit information respectively provided by the M-dimensional feature signals, wherein a length of the bit information provided by the feature signal of each dimension is proportional to a degree of distinguishability determined by a compactness of the feature distribution of the authentic user with respect to a diversity of global feature distribution of all users in a corresponding dimension, wherein the stable key generation step comprises: a setting step for setting a left boundary and a right boundary of the feature signal distribution in each dimension as LB+min (mg−kgσg, ma−kaσa) and RB=max(mg+kgσg, ma+kaσa) respectively, where ma and σa are mean and standard deviation of the authentic feature signal distribution in the corresponding dimension, mg and σg are mean and standard deviation of a global feature signal distribution in the corresponding dimension, ka and kg are parameters, and (ma−kaσa, ma+kaσa) is specified as an authentic region; a distinguishing step for dividing at least one segment between the left boundary and the right boundary in the corresponding dimension according to the authentic region, wherein a length of each segment is proportional to the length of the authentic region; an index specifying step for specifying each segment with an index thereby obtaining authentic bit information provided by the feature signal in the corresponding dimension, wherein the length of bit information is proportional to a degree of distinguishability in the dimension; and a cascading step for cascading the bit information provided by the feature signal in each corresponding dimension for obtaining a cryptographic key. 15. The method as claimed in claim 14, wherein the cascading step cascades the bit information provided by the M-dimensional feature signals.