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

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

  • Multi-biometric Cryptosystems Based on Feature-Level Fusion Presented By
  • Outline  Literature Survey  Objective  Significance  Proposed Methodology  S/W and H/W Technology  Mathematical Model
  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • Mathematical Model Biometric Cryptosystems Constrained Biometric Security analysis Embedding Algorithm