Index Of Maximum Research Articles

Secure Chaff-less Fuzzy Vault for Face Identification Systems

Biometric cryptosystems such as fuzzy vaults represent one of the most popular approaches for secret and biometric template protection. However, they are solely designed for biometric verification, where the user is required to input both identity credentials and biometrics. Several practical questions related to the implementation of biometric cryptosystems remain open, especially in regard to biometric template protection. In this article, we propose a face cryptosystem for identification (FCI) in which only biometric input is needed. Our FCI is composed of a one-to-N search subsystem for template protection and a one-to-one match chaff-less fuzzy vault (CFV) subsystem for secret protection. The first subsystem stores N facial features, which are protected by index-of-maximum (IoM) hashing, enhanced by a fusion module for search accuracy. When a face image of the user is presented, the subsystem returns the top k matching scores and activates the corresponding vaults in the CFV subsystem. Then, one-to-one matching is applied to the k vaults based on the probe face, and the identifier or secret associated with the user is retrieved from the correct matched vault. We demonstrate that coupling between the IoM hashing and the CFV resolves several practical issues related to fuzzy vault schemes. The FCI system is evaluated on three large-scale public unconstrained face datasets (LFW, VGG2, and IJB-C) in terms of its accuracy, computation cost, template protection criteria, and security.

Secure Secret Sharing Enabled b-band Mini Vaults Bio-Cryptosystem for Vectorial Biometrics

Biometric Cryptosystems for secret binding such as fuzzy vault and fuzzy commitment are provable secure and offers a convenient way for secret management and protection. Despite numerous practical schemes have been reported, they are deficient in resisting several security and privacy attacks. In this paper, we propose a novel bio-cryptosystem that based on the three key ingredients namely Index of Maximum (IoM) hashing, $(m, k)$ ( m , k ) threshold secret sharing and b -band mini vaults notion. The IoM hashing is motivated from the ranking based Locality Sensitive Hashing theory meant for non-invertible transformation. On the other hand, the $(m, k)$ ( m , k ) threshold secret sharing scheme and the b -band mini vaults manage overcome inherent limitations of biometric cryptosystems when integrated with IoM hashing. The proposed scheme strikes the balance between performance and the privacy/security protection. Unlike fuzzy vault and fuzzy commitment, which primarily devised for unordered and binary biometrics, respectively, our scheme is tailored for feature vector-based biometrics (vectorial biometrics). Comprehensive experiments on fingerprint vectors that derived from several FVC fingerprint benchmarks and rigorous analysis demonstrate decent secret retrieval performance yet offer strong resilience against six major security and privacy attacks.

Finger Vein Template Protection Based on Alignment-Robust Feature Description and Index-of-Maximum Hashing

Privacy preserving storage and secure processing of biometric data is a key issue that has to be addressed in finger vein recognition systems as well. Various template protection approaches originally proposed for well established biometric modalities have been adopted to the domain of finger vein authentication. However, these adopted methods have the disadvantage that they are not designed for finger vein patterns in particular and are thus, sub-optimal in several ways. In this study we propose an alignment-robust template protection scheme that is based on an efficient binary representation of finger vein patterns on the one hand and is further using the advantages of Index-of-Maximum (IoM) hashing to fulfill mandatory privacy and security based characteristics on the other hand. The proposed method is compared to block re-mapping and warping regarding recognition performance and is analyzed with respect to security and privacy aspects. The analysis is further extended to robustness against misalignment of the finger vein data and a combination of block re-mapping/warping and the proposed method is investigated as well.

Open-set face identification with index-of-max hashing by learning

Large-scale face identification or 1-to-N matching where N is huge, plays a vital role in biometrics and surveillance. The system demands accurate and speedy matching where compact facial feature representation and a simple matcher are favored. On the other hand, most research considers closed-set identification that assumes that all identities of probe samples are enclosed in the gallery. On the contrary, open-set identification expects that some probe identities are not known to the system. This setup poses an additional challenge, where the system should be able to reject those probes that correspond to unknown identities. In this paper, we address the large-scale open-set face identification problem with a compact facial representation that is based on the index-of-maximum (IoM) hashing, which was designed for biometric template protection. To be specific, the existing random IoM hashing is advanced to a data-driven based hashing technique, where the hashed face code can be made compact and matching can be easily performed by the Hamming distance, which can offer highly efficient matching. Furthermore, since IoM hashing transforms the original facial features non-invertibly, the privacy of users can also be preserved. Along with IoM hashed face code, we explore several fusion strategies to address the open-set face identification problem. The comprehensive evaluations are carried out with three large-scale unconstrained face datasets, namely LFW, VGG2 and IJB-C.