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Multi biometric cryptosystems based on feature-level fusion
Multi biometric cryptosystems based on feature-level fusion
Multi biometric cryptosystems based on feature-level fusion
Multi biometric cryptosystems based on feature-level fusion
Multi biometric cryptosystems based on feature-level fusion
Multi biometric cryptosystems based on feature-level fusion
Multi biometric cryptosystems based on feature-level fusion
Multi biometric cryptosystems based on feature-level fusion
Multi biometric cryptosystems based on feature-level fusion
Multi biometric cryptosystems based on feature-level fusion
Multi biometric cryptosystems based on feature-level fusion
Multi biometric cryptosystems based on feature-level fusion
Multi biometric cryptosystems based on feature-level fusion
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Multi biometric cryptosystems based on feature-level fusion

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  1. Multi-biometric Cryptosystems Based on Feature-Level Fusion Presented By
  2. Outline  Literature Survey  Objective  Significance  Proposed Methodology  S/W and H/W Technology  Mathematical Model
  3. Literature survey  Sutcu combined face and fingerprint templates that are both transformed into binary strings. These binary strings are concatenated and used as the input to a fuzzy commitment scheme.  Nanda-kumar and Jain proposed a multi-biometric cryptosystem in which biometric templates based on binary strings and point-sets are combined. The binary string is divided into a number of segments and each segment is separately secured using a fuzzy commitment scheme. The keys associated with these segment-wise fuzzy commitment schemes are then used as additional points in the fuzzy vault constructed using the point-set-based features.  Fu theoretically analyzed the template security and recognition accuracy imparted by a multi-biometric cryptosystem, which can be operated in four different ways: no-split, MN-split, package, and biometric model.
  4. Objective To perform and analyze research work on Multi-biometric cryptosystems that uses the knowledge from the areas of statistical pattern recognition, biometric recognition, computer vision and image processing and develop innovative solutions that allow secure, convenient, and. cost- effective interaction between humans and machines.
  5. Significance  Multi-biometrics are used world-wide because there will be low error rate and large population coverage.  The multibiometric cryptosystems proposed here have higher security and matching performance compared to their unibiometric counterparts.  Multibiometric systems are being widely adopted inmany large-scale identification systems, including the FBI’s IAFIS, the Department of Homeland Security’s US-VISIT, and the Government of India’s UID.
  6. Proposed Methodology  In our study we propose a feature level framework for multi-biometric multi- systems that consists of the three basic modules :  Embedding Algorithm  Fusion Module  Biometric Multi-systems  Security Analysis
  7. Embedding Algorithm In embedding algorithm, The embedding algorithm transforms a biometric feature representation into a new feature representation zm , where zm= Σ (m)xm , for all m=1,2,……M. The input representation can be a real-valued feature vector, a binary string, or a point-set. The output representation could be a binary string or a point-set that could be secured using fuzzy commitment or fuzzy vault, respectively.
  8. Fusion Module The fusion module combines a set of homogeneous biometric features Z={z1,z2,z3…zm} to generate a fused multibiometric feature representation z. For point-set-based representations, one can use Cs(Z)=Um=1 Zm. In the case of binary strings, the fused feature vector can be obtained by simply concatenating the individual strings, i.e., z=Cb(Z)=[Z1,Z2,Z3…Zm]. Note that it is also possible to define more complex fusion schemes, where features could be selected based on criteria such as reliability and discriminability.
  9. Biometric Cryptosystems In biometric cryptosystem , the biometric cryptosystem generates a secure sketch Yc the fused feature vector Ze(obtained from the set of biometric templates ) and a key , i.e. During authentication, the biometric templates XE={ X1E ,X2E ……XE M} and a key kc .During cryptosystem recovers Kc from Yc and ZA. Fuzzy commitment is used if is a binary string, whereas a fuzzy vault is used if is a point-set.
  10. Security Analysis Each of the above three modules play a critical role in determining the matching performance and security of the multi-biometric cryptosystem. We propose fairly simple algorithms for implementing the above three modules and do not focus on optimizing them.
  11. H/W and S/W requirement Hard disk 80 GB RAM 512 MB Processor Intel Pentium IV Technology Java Tools Net-Beans IDE + JDK Operating System Windows
  12. Mathematical Model Biometric Cryptosystems Constrained Biometric Security analysis Embedding Algorithm

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