Although biometrics is more reliable, robust and convenient than traditional methods, security and privacy concerns are growing. Biometric templates stored in databases are vulnerable to attacks if they are not protected. To solve this problem, a biometric cryptosystem approach that combines cryptography and biometrics has been proposed. Under this approach, helper data is stored in a database rather than the original reference biometric templates. The helper data is generated from the original reference biometric templates and a cryptographic key with error-correcting coding schemes. During decoding, the same cryptographic key can be released from the helper data if and only if the input query data is close enough to the reference. It is assumed that the helper data does not reveal any information about the original reference biometric templates. Thus, the biometric cryptosystem approach can protect the original reference templates. However, error-correcting coding algorithms (e.g., the fuzzy commitment scheme and fuzzy vault) normally require finite input. As most face templates are real-valued templates, a binarization scheme transforming the original real-valued face templates into binary templates is required. Most existing binarization schemes are performed in an ad hoc manner and do not consider the discriminability of the binary template. The recognition accuracy based on the binary templates is thus degraded. In view of this limitation, we propose a new binarization scheme by optimizing binary template discriminability. A novel binary discriminant analysis is developed to transform a real-valued template into a binary template. Differentiation is hard to perform in binary space and direct optimization is difficult. To solve this problem, we construct a continuous function based on the perceptron to optimize binary template discriminability. Our experimental results show that the proposed algorithm improves binary template discriminability.

Biometric cryptosystem (BC) is a very secure approach for template protection because the stored template is encrypted. The key issues in BC approach include(i) limited capability in handling intra-class variations and (ii) binary input is required. To overcome these problems, this paper adopts the concept of discriminative analysis and develops a new binary discriminant analysis (BDA) method to convert a real valued template to a binary template. Experimental results on CMU-PIE and FRGC face databases show that the proposed BDA method outperforms existing template binarization schemes.

Biometric template protection is one of the most important issues in deploying a practical biometric system. To tackle this problem, many algorithms, that do not store the template in its original form, have been reported in recent years. They can be categorized into two approaches, namely biometric cryptosystem and transform-based. However, most (if not all) algorithms in both approaches offer a trade-off between the template security and matching performance. Moreover, we believe that no single template protection method is capable of satisfying the security and performance simultaneously. In this paper, we propose a hybrid approach which takes advantage of both the biometric cryptosystem approach and the transform-based approach. A three-step hybrid algorithm is designed and developed based on random projection, discriminability-preserving (DP) transform, and fuzzy commitment scheme. The proposed algorithm not only provides good security, but also enhances the performance through the DP transform. Three publicly available face databases, namely FERET, CMU-PIE, and FRGC, are used for evaluation. The security strength of the binary templates generated from FERET, CMU-PIE, and FRGC databases are 206.3, 203.5, and 347.3 bits, respectively. Moreover, noninvertibility analysis and discussion on data leakage of the proposed hybrid algorithm are also reported. Experimental results show that, using Fisherface to construct the input facial feature vector (face template), the proposed hybrid method can improve the recognition accuracy by 4%, 11%, and 15% on the FERET, CMU-PIE, and FRGC databases, respectively. A comparison with the recently developed random multispace quantization biohashing algorithm is also reported.

This paper addresses the face data variations problem in biometric cryptosystems in which the cryptographic technique is applied to biometric system. To overcome the limitation, this paper introduce a new class-distribution preserving transform to biometric cryptosystems. The basic idea is to transform a real value face feature vector to a binary feature vector using a random points set. The proposed transform is integrated into a BCH coding technique. Fisherface algorithm is used for feature extraction and ORL face database is selected for experiments. It is shown that only around 0.8% accuracy is degraded in comparing with the original Fisherface algorithm while the system security can be enhanced by 126 bits.