Multi-biometric Cryptosystem Research Articles

BIOFUSE: A framework for multi-biometric fusion on biocryptosystem level

Biometric cryptosystems or biocryptoystems are gaining prominence for cryptographic key generation, encryption and biometric template protection. However, the most popular state-of-the-art biocryptosystems- fuzzy commitment and fuzzy vault are prone to multiple security attacks. Recently proposed multi-biometric cryptosystems improve security and enhance recognition performance. They perform the fusion of multi-biometric characteristics with either a single biocryptosystem or independently accessed, multiple biocryptosystems. An attack on any of the involved biocryptosystems can weaken the security of the whole system. In our paper, we propose a multi-biometric fusion framework- BIOFUSE, that combines fuzzy commitment and fuzzy vault using the format-preserving encryption scheme. BIOFUSE makes it improbable for an attacker to get unauthorized access to the system without impersonation of all the biometric inputs of the genuine user at the same instant. We present 4 most basic ways of constructing BIOFUSE and found only 1 named S-BIOFUSE (S3) as a secure design. We compare the recognition performance of the proposed scheme with existing multi-biometric cryptosystems on various databases. The results show 0.98 true match rate at 0.01 false match rate on a virtual IITD-DB1 database that indicates that our proposed work achieves significantly good recognition performance while providing high security.

A comprehensive overview of biometric fusion

The performance of a biometric system that relies on a single biometric modality (e.g., fingerprints only) is often stymied by various factors such as poor data quality or limited scalability. Multibiometric systems utilize the principle of fusion to combine information from multiple sources in order to improve recognition accuracy whilst addressing some of the limitations of single-biometric systems. The past two decades have witnessed the development of a large number of biometric fusion schemes. This paper presents an overview of biometric fusion with specific focus on three questions: what to fuse, when to fuse, and how to fuse. A comprehensive review of techniques incorporating ancillary information in the biometric recognition pipeline is also presented. In this regard, the following topics are discussed: (i) incorporating data quality in the biometric recognition pipeline; (ii) combining soft biometric attributes with primary biometric identifiers; (iii) utilizing contextual information to improve biometric recognition accuracy; and (iv) performing continuous authentication using ancillary information. In addition, the use of information fusion principles for presentation attack detection and multibiometric cryptosystems is also discussed. Finally, some of the research challenges in biometric fusion are enumerated. The purpose of this article is to provide readers a comprehensive overview of the role of information fusion in biometrics.

Multi-Biometric Cryptosystem Based on Generation of Graphs Using Feature Vectors

Multi-Biometric Cryptosystem Based on Generation of Graphs Using Feature Vectors

Binary feature fusion for discriminative and secure multi-biometric cryptosystems

Biometric cryptosystem has been proven to be a promising approach for template protection. Cryptosystems such as fuzzy extractor and fuzzy commitment require discriminative and informative binary biometric input to offer accurate and secure recognition. In multi-modal biometric recognition, binary features can be produced via fusing the real-valued unimodal features and binarizing the fused features. However, when the extracted features of certain modality are represented in binary and the extraction parameters are not known, real-valued features of other modalities need to be binarized and the feature fusion needs to be carried out at the binary level. In this paper, we propose a binary feature fusion method that extracts a set of fused binary features with high discriminability (small intra-user and large inter-user variations) and entropy (weak dependency among bits and high bit uniformity) from multiple sets of binary unimodal features. Unlike existing fusion methods that mainly focus on discriminability, the proposed method focuses on both feature discriminability and system security: The proposed method 1) extracts a set of weakly dependent feature groups from the multiple unimodal features; and 2) fuses each group to a bit using a mapping that minimizes the intra-user variations and maximizes the inter-user variations and uniformity of the fused bit. Experimental results on three multi-modal databases show that fused binary feature of the proposed method has both higher discriminability and higher entropy compared to the unimodal features and the fused features generated from the state-of-the-art binary fusion approaches.

A New Biocryptosystem-Oriented Security Analysis Framework and Implementation of Multibiometric Cryptosystems Based on Decision Level Fusion

Biometric cryptosystems provide an innovative solution for cryptographic key generation, encryption as well as biometric template protection. Besides high authentication accuracy, a good biometric cryptosystem is expected to protect biometric templates effectively, which requires that helper data does not reveal significant information about the templates. Previous works predominantly follow an appropriate entropy definition to measure the security of biometric cryptosystems. In this paper, we point out limitations of entropy-based security analysis and propose a new security analysis framework that combines information-theoretic approach with computational security. In addition, we construct a fingerprint-based multibiometric cryptosystem (MBC) using decision level fusion. Hash functions are employed in our construction to further protect each single biometric trait. The experimental results and security analysis demonstrate that the proposed MBC provides stronger security and better authentication accuracy compared with a cryptosystem based on single biometric.

Finger multibiometric cryptosystem based on score-level fusion

As a very promising approach, multibiometrics has been extensively studied to overcome the limitations of unibiometrics for high recognition performance in recent years. However, multibiometric systems always store multiple biometric templates for each user, which causes significant risks to user privacy and system security. In this paper, we address two critical issues in the design of finger multibiometric system, i.e., fusion methodology and template security. First, the score-level fusion framework using Fuzzy Commitment Scheme is proposed to protect multiple finger biometric templates of fingerprint, finger vein, finger knuckle print and finger shape traits. Second, the security of the proposed multibiometric cryptosystem is analysed on the basis of the correcting capacity of Error Correcting Code. The experimental results and security evaluation are conducted on a merged finger multimodal biometrics database. The comparative experiments show that the proposed finger multibiometric cryptosystem outperforms the unibiometric counterparts in terms of verification performance and template security.

Palm and hand vein-based fuzzy vault generation scheme for multibiometric cryptosystem

Template security of biometric systems is a vital issue and needs critical focus. The importance lies in the fact that unlike passwords, stolen biometric templates cannot be revoked. Hence, the biometric templates cannot be stored in plain format and needs strong protection against any forgery. In this paper, we present a technique to generate face and palm vein-based fuzzy vault for multi-biometric cryptosystem. Here, initially the input images are pre-processed using various processes to make images fit for further processing. In our proposed method, the features are extracted from the processed hand and palm vein images by finding out unique common points. The chaff points are added to the already extracted points to obtain the combined feature vector. The secret key points which are generated based on the user key input (by using proposed method) are added to the combined feature vector to have the fuzzy vault. For decoding, the multi-modal biometric template from palm vein and hand vein image is constructed and is combined with the stored fuzzy vault to generate the final key. Finally, the experimentation is conducted using the palm vein and hand vein database. The evaluation metrics employed are FMR (False Match Ratio) and GMR (Genuine Match Ratio). From the metric values obtained for the proposed system, we can infer that the system has performed well. The results are also compared to our earlier technique and the results shows that the proposed technique has achieved better results having GAR of 94% without noise and 88% with noise.

Finger Multi-biometric Cryptosystem using Feature-Level Fusion

In this paper, we propose a new finger multi-biometric cryptosystem using feature-level fusion to simultaneously protect multiple templates of fingerprint, finger vein, finger knuckle print and finger shape traits as a single secure sketch. We theoretically analyze the featurelevel fusion for finger multi-biometric cryptosystem with respect to their impact on security and recognition accuracy. The comparative experimental results ascertain that finger multibiometric cryptosystem outperforms the uni-biometric counterparts in terms of verification performance and template security.

Technique to generate face and palm vein-based fuzzy vault for multi-biometric cryptosystem

Template security of biometric systems is a vital issue and needs critical focus. The importance lies in the fact that unlike passwords, stolen biometric templates cannot be revoked. Hence, the biometric templates cannot be stored in plain format and needs strong protection against any forgery. In this paper, we present a technique to generate face and palm vein-based fuzzy vault for multi-biometric cryptosystem. Here, initially the input images are pre-processed using various processes to make images fit for further processing. In our proposed method, the features are extracted from the processed face and palm vein images by finding out unique common points. The chaff points are added to the already extracted points to obtain the combined feature vector. The secret key points which are generated based on the user key input (by using proposed method) are added to the combined feature vector to have the fuzzy vault. For decoding, the multi-modal biometric template from palm vein and face image is constructed and is combined with the stored fuzzy vault to generate the final key. Finally, the experimentation is conducted using the palm vein and face database available in the CASIA and JAFFE database. The evaluation metrics employed are FMR (False Match Ratio) and GMR (Genuine Match Ratio). From the metric values obtained for the proposed system, we can infer that the system has performed well.

Finger multibiometric cryptosystems: fusion strategy and template security

We address two critical issues in the design of a finger multibiometric system, i.e., fusion strategy and template security. First, three fusion strategies (feature-level, score-level, and decision-level fusions) with the corresponding template protection technique are proposed as the finger multibiometric cryptosystems to protect multiple finger biometric templates of fingerprint, finger vein, finger knuckle print, and finger shape modalities. Second, we theoretically analyze different fusion strategies for finger multibiometric cryptosystems with respect to their impact on security and recognition accuracy. Finally, the performance of finger multibiometric cryptosystems at different fusion levels is investigated on a merged finger multimodal biometric database. The comparative results suggest that the proposed finger multibiometric cryptosystem at feature-level fusion outperforms other approaches in terms of verification performance and template security.

Multibiometric Cryptosystems Based on Feature-Level Fusion

Multibiometric systems are being increasingly de- ployed in many large-scale biometric applications (e.g., FBI-IAFIS, UIDAI system in India) because they have several advantages such as lower error rates and larger population coverage compared to unibiometric systems. However, multibiometric systems require storage of multiple biometric templates (e.g., fingerprint, iris, and face) for each user, which results in increased risk to user privacy and system security. One method to protect individual templates is to store only the secure sketch generated from the corresponding template using a biometric cryptosystem. This requires storage of multiple sketches. In this paper, we propose a feature-level fusion framework to simultaneously protect multiple templates of a user as a single secure sketch. Our main contributions include: (1) practical implementation of the proposed feature-level fusion framework using two well-known biometric cryptosystems, namery,fuzzy vault and fuzzy commitment, and (2) detailed analysis of the trade-off between matching accuracy and security in the proposed multibiometric cryptosystems based on two different databases (one real and one virtual multimodal database), each containing the three most popular biometric modalities, namely, fingerprint, iris, and face. Experimental results show that both the multibiometric cryptosystems proposed here have higher security and matching performance compared to their unibiometric counterparts.

Multibiometric Cryptosystem: Model Structure and Performance Analysis

Single biometric cryptosystems were developed to obtain win-win scenarios for security and privacy. They are seriously threatened by spoof attacks, in which a forged biometric copy or artificially recreated biometric data of a legitimate user may be used to spoof a system. Meanwhile, feature alignment and quantization greatly degrade the accuracy of single biometric cryptosystems. In this paper, by trying to bind multiple biometrics to cryptography, a cryptosystem named multibiometric cryptosystem (MBC), is demonstrated from the theoretical point of view. First, an MBC with two fusion levels: fusion at the biometric level, and fusion at the cryptographic level, is formally defined. Then four models, namely biometric fusion model, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MN</i> -split model, nonsplit model, and package model, adopted at those two levels for fusion are presented. Shannon entropy analysis shows that even if the biometric ciphertexts and some biometric traits are disclosed, the new constructions still can achieve consistently data security and biometric privacy. In addition, the achievable accuracy is analyzed in terms of false acceptance rate/false rejection rate at each model. Finally, a comparison on the relative advantages and disadvantages of the proposed models is discussed.