Composite fixed-length ordered features with index-of-max transformation for high-performing and secure palmprint template protection

Zhicheng Cao , Weiqiang Zhao , Heng Zhao, Liaojun Pang
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Abstract

Palmprint recognition has attracted considerable attention due to its advantages over other biometric modalities such as fingerprints, in that it is larger in area, richer in information and able to work at a distance. However, the issue of palmprint privacy and security (especially palmprint template protection) remains under-studied. Among the very few research works, most of them only use orientational features of the palmprint with transformation processing, yielding unsatisfactory recognition and protection performance. Thus, this research work proposes a palmprint feature extraction method for palmprint template protection that is fixed-length and ordered in nature, by fusing point features and orientational features. Firstly, dual orientations are extracted and encoded with more accuracy based on the modified finite Radon transform (MFRAT). Then, SURF feature points are extracted and converted to be fixed-length and ordered features. Finally, composite fixed-length ordered features that fuse up the dual orientations and SURF points are transformed using the irreversible transformation of index-of-max (IoM) to generate the revocable palmprint templates. Experiments show that the matching accuracy of the proposed method of fixed-length and ordered point features are superior to all other feature extraction methods on the PolyU and CASIA datasets. It is also demonstrated that the EERs before and after IoM transformation are better than all other representative template protection methods. A thorough security and privacy analysis including brute-force attack, false accept attack, birthday attack, attack via record multiplicity, irreversibility, unlinkability and revocability is also given, which proves that our proposed method has both high performance and security.

Abstract Image

具有最大索引变换的复合定长有序特征,用于高性能和安全的掌纹模板保护
相对于指纹等其他生物识别方式,掌纹识别具有面积更大、信息更丰富、能够远距离工作等优点,因此受到了广泛的关注。然而,掌纹隐私和安全问题(特别是掌纹模板保护)仍未得到充分研究。在为数不多的研究工作中,大多数只利用掌纹的方向特征进行变换处理,识别和保护效果不理想。因此,本研究提出了一种融合点特征和方向特征的定长有序掌纹模板保护掌纹特征提取方法。首先,基于改进有限Radon变换(MFRAT)对对偶取向进行提取和编码,提高了对偶取向的精度;然后,提取SURF特征点并将其转换为定长有序特征。最后,利用最大索引(index-of-max, IoM)的不可逆变换,对融合双方向点和SURF点的复合定长有序特征进行变换,生成可撤销掌纹模板。实验结果表明,该方法在PolyU和CASIA数据集上对固定长度和有序点特征的匹配精度优于所有其他特征提取方法。结果表明,IoM变换前后的EERs优于其他所有代表性模板保护方法。对该方法进行了全面的安全性和隐私性分析,包括暴力攻击、虚假接受攻击、生日攻击、记录多重性攻击、不可逆性、不可链接性和可撤销性,证明了该方法具有高性能和安全性。
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