Palmprint recognition has attracted considerable attention due to its advantages over other biometric modalities such as fingerprints,in that it is larger in area,richer in information and able to work at a distance.H...Palmprint recognition has attracted considerable attention due to its advantages over other biometric modalities such as fingerprints,in that it is larger in area,richer in information and able to work at a distance.However,the issue of palmprint privacy and security(especially palmprint template protection)remains under-studied.Among the very few research works,most of them only use orientational features of the palmprint with transformation processing,yielding unsatisfactory recognition and protection performance.Thus,this research work proposes a palmprint feature extraction method for palmprint template protection that is fixed-length and ordered in nature,by fusing point features and orientational features.Firstly,dual orientations are extracted and encoded with more accuracy based on the modified finite Radon transform(MFRAT).Then,SURF feature points are extracted and converted to be fixed-length and ordered features.Finally,composite fixed-length ordered features that fuse up the dual orientations and SURF points are transformed using the irreversible transformation of index-of-max(IoM)to generate the revocable palmprint templates.Experiments show that the matching accuracy of the proposed method of fixed-length and ordered point features are superior to all other feature extraction methods on the PolyU and CASIA datasets.It is also demonstrated that the EERs before and after IoM transformation are better than all other representative template protection methods.A thorough security and privacy analysis including brute-force attack,false accept attack,birthday attack,attack via record multiplicity,irreversibility,unlinkability and revocability is also given,which proves that our proposed method has both high performance and security.展开更多
The paper is devoted to the optimization of data structure in classification and clustering problems by mapping the original data onto a set of ordered feature vectors.When ordering,the elements of each feature vector...The paper is devoted to the optimization of data structure in classification and clustering problems by mapping the original data onto a set of ordered feature vectors.When ordering,the elements of each feature vector receive new num-bers such that their values are arranged in non-decreasing order.For update structure,the main volume of computational operations is performed not on multidimensional quantities describing objects,but on one-dimensional ones,which are the values of objects individual features.Then,instead of a rather complex existing algorithm,the same simplest algorithm is repeatedly used.Transition from original to ordered data leads to a decrease in the entropy of data distribution,which allows us to reveal their properties.It was shown that the classes differ in the functions of feature values for ordered object numbers.The set of these functions displays the information contained in the training sample and allows one to calculate class of any object in the test sample by values of its features using the simplest total probability formula.The paper also discusses the issues of using ordered data matrix to solve problems of par titioning a set into clusters of objects that have common properties.展开更多
基金funded by the National Natural Science Foundation of China(61906149)the Key Research and Development Program of Shaanxi(2024GX-YBXM-543)+2 种基金the Natural Science Basic Research Program of Shaanxi(2024JC-YBMS-471)the Chunhui Project of the Ministry of Education of China(202200927)the Fundamental Research Funds for the Central Universities(ZYTS24150).
文摘Palmprint recognition has attracted considerable attention due to its advantages over other biometric modalities such as fingerprints,in that it is larger in area,richer in information and able to work at a distance.However,the issue of palmprint privacy and security(especially palmprint template protection)remains under-studied.Among the very few research works,most of them only use orientational features of the palmprint with transformation processing,yielding unsatisfactory recognition and protection performance.Thus,this research work proposes a palmprint feature extraction method for palmprint template protection that is fixed-length and ordered in nature,by fusing point features and orientational features.Firstly,dual orientations are extracted and encoded with more accuracy based on the modified finite Radon transform(MFRAT).Then,SURF feature points are extracted and converted to be fixed-length and ordered features.Finally,composite fixed-length ordered features that fuse up the dual orientations and SURF points are transformed using the irreversible transformation of index-of-max(IoM)to generate the revocable palmprint templates.Experiments show that the matching accuracy of the proposed method of fixed-length and ordered point features are superior to all other feature extraction methods on the PolyU and CASIA datasets.It is also demonstrated that the EERs before and after IoM transformation are better than all other representative template protection methods.A thorough security and privacy analysis including brute-force attack,false accept attack,birthday attack,attack via record multiplicity,irreversibility,unlinkability and revocability is also given,which proves that our proposed method has both high performance and security.
文摘The paper is devoted to the optimization of data structure in classification and clustering problems by mapping the original data onto a set of ordered feature vectors.When ordering,the elements of each feature vector receive new num-bers such that their values are arranged in non-decreasing order.For update structure,the main volume of computational operations is performed not on multidimensional quantities describing objects,but on one-dimensional ones,which are the values of objects individual features.Then,instead of a rather complex existing algorithm,the same simplest algorithm is repeatedly used.Transition from original to ordered data leads to a decrease in the entropy of data distribution,which allows us to reveal their properties.It was shown that the classes differ in the functions of feature values for ordered object numbers.The set of these functions displays the information contained in the training sample and allows one to calculate class of any object in the test sample by values of its features using the simplest total probability formula.The paper also discusses the issues of using ordered data matrix to solve problems of par titioning a set into clusters of objects that have common properties.
