Computational workload in biometric identification systems: an overview <s> Binarisation: <s> In this paper, we introduce the Minutia Cylinder-Code (MCC): a novel representation based on 3D data structures (called cylinders), built from minutiae distances and angles. The cylinders can be created starting from a subset of the mandatory features (minutiae position and direction) defined by standards like ISO/IEC 19794-2 (2005). Thanks to the cylinder invariance, fixed-length, and bit-oriented coding, some simple but very effective metrics can be defined to compute local similarities and to consolidate them into a global score. Extensive experiments over FVC2006 databases prove the superiority of MCC with respect to three well-known techniques and demonstrate the feasibility of obtaining a very effective (and interoperable) fingerprint recognition implementation for light architectures. <s> BIB001 </s> Computational workload in biometric identification systems: an overview <s> Binarisation: <s> Biometrics refers to physiological (i.e., face, fingerprint, hand geometry, etc.) and behavioral (i.e., speech, signature, keystroke, etc.) traits of a human identity. As these traits are unique to individuals, biometrics can be used to identify users reliably in many authentication applications, such as access control and e-commerce. Most biometric authentication systems offer great convenience without requiring the users to possess or remember any secret credentials. For applications that demand greater security, biometrics can be used in complement with passwords and security tokens to offer a multifactor authentication. <s> BIB002 </s> Computational workload in biometric identification systems: an overview <s> Binarisation: <s> Local binary patterns (LBP) represent a well-established technique for reliable facial recognition. Multi-scale block (MB) LBP, which process average pixel values of block sub-regions instead of single pixels, have been proposed in order to achieve more robust feature vectors, which consist of histograms of (MB)-LBP values. In this work, we present a simple, yet effective, method to binarize histograms obtained from a MB-LBP face recognition system. The proposed method extracts compact binary feature vectors which allow for a rapid comparison and which are required for privacy enhanced biometric processing, when biometric template protection is enabled. On the publicly available FERET and Extended-Yale-B dataset, the presented scheme reveals only a negligible drop in biometric performance compared to the original MB-LBP system, while a substantial speed-up is achieved in the comparison stage. <s> BIB003 </s> Computational workload in biometric identification systems: an overview <s> Binarisation: <s> Feature vectors extracted from biometric characteristics are often represented using floating point values. It is, however, more appealing to store and compare feature vectors in a binary representation, since it generally requires less storage and facilitates efficient comparators which utilise intrinsic bit operations. Furthermore, the binary representations are very often necessary for some specific application scenarios, e.g. template protection and indexing. In recent years, usage of deep neural networks for facial recognition has vastly improved the biometric performance of said systems. In this paper, various binarisation schemes are applied to such feature vectors and benchmarked for biometric performance. It is shown that with only a negligible drop in biometric performance, the storage space and computational requirements can be vastly decreased. <s> BIB004
Comparison of float-based feature vectors is relatively expensive computationally, due to use of comparators based on e.g. Euclidean or χ 2 distances. In many cases, such feature vectors can be quantised and encoded into binary strings, whereby utilisation of comparators based on e.g. Hamming distance is possible. Such comparators can take advantage of the more efficient bitwise operators, thereby reducing the computational workload. An illustrative example can be seen in BIB004 (and a simpler one in BIB003 ), where various bit allocation schemes for float-based feature vectors generated by neural network-based systems are benchmarked. In BIB001 , a new representation is extracted from minutiae points, which can be further binarised to accelerate the biometric template comparisons. Although some information is lost through the binarisation process, both publications show only negligible biometric performance loss in relation to their respective baselines, while achieving a significant speed-up. Finally, binarised feature vectors are an essential component in the context of many template protection schemes (see e.g. BIB002 for more details on this subject).