A hybrid approach for face template protection

Abstract

Biometric template protection is one of the important issues in deploying a practical biometric system. To tackle this problem, many algorithms have been reported in recent years, most of them being applicable to fingerprint biometric. Since the content and representation of fingerprint template is different from templates of other modalities such as face, the fingerprint template protection algorithms cannot be directly applied to face template. Moreover, we believe that no single template protection method is capable of satisfying the diversity, revocability, security and performance requirements. We propose a three-step cancelable framework which is a hybrid approach for face template protection. This hybrid algorithm is based on the random projection, class distribution preserving transform and hash function. Two publicly available face databases, namely FERET and CMU-PIE, are used for evaluating the template protection scheme. Experimental results show that the proposed method maintains good template discriminability, resulting in good recognition performance. A comparison with the recently developed random multispace quantization (RMQ) biohashing algorithm shows that our method outperforms the RMQ algorithm.

Visual Cryptography for Biometric Privacy

“…This representation does not include any information about the connection between landmarks. Thus, (4) where is the number of landmarks used to locate and annotate face features.…”

“…In the following experiments, the match scores were generated using the Verilook SDK. 4 In order to establish a baseline, the images in the private database were first matched against each other. This resulted in an EER of 6% for the IMM database and 2% for the XM2VTS database.…”

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