In this paper we show the feasibility of template protecting biometric authentication systems. In particular, we apply template protection schemes to fingerprint data. Therefore we first make a fixed length representation of the fingerprint data by applying Gabor filtering. Next we introduce the reliable components scheme. In order to make a binary representation of the fingerprint images we extract and then quantize during the enrollment phase the reliable components with the highest signal to noise ratio. Finally, error correction coding is applied to the binary representation. It is shown that the scheme achieves an EER of approximately 4.2% with secret length of 40 bits in experiments.

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Preserving the privacy of biometric information stored in biometric systems is becoming a key issue. An important element in privacy protecting biometric systems is the quantizer which transforms a normal biometric template into a binary string. In this paper, we present a user-specific quantization method based on a likelihood ratio approach (LQ). The bits generated from every feature are concatenated to form a fixed length binary string that can be hashed to protect its privacy. Experiments are carried out on both fingerprint data (FVC2000) and face data (FRGC). Results show that our proposed quantization method achieves a reasonably good performance in terms of FAR/FRR (when FAR is 10 −4 , the corresponding FRR are 16.7% and 5.77% for FVC2000 and FRGC, respectively).

Extracting binary strings from real-valued biometric templates is a fundamental step in many biometric template protection systems, such as fuzzy commitment, fuzzy extractor, secure sketch, and helper data systems. Previous work has been focusing on the design of optimal quantization and coding for each single feature component, yet the binary string-concatenation of all coded feature components-is not optimal. In this paper, we present a detection rate optimized bit allocation (DROBA) principle, which assigns more bits to discriminative features and fewer bits to nondiscriminative features. We further propose a dynamic programming (DP) approach and a greedy search (GS) approach to achieve DROBA. Experiments of DROBA on the FVC2000 fingerprint database and the FRGC face database show good performances. As a universal method, DROBA is applicable to arbitrary biometric modalities, such as fingerprint texture, iris, signature, and face. DROBA will bring significant benefits not only to the template protection systems but also to the systems with fast matching requirements or constrained storage capability.