Scalable persistent memory (PM) has opened up new opportunities for building indexes that operate and persist data directly on the memory bus, potentially enabling instant recovery, low latency and high throughput. When real PM hardware (Intel Optane Persistent Memory) first became available, previous work evaluated PM indexes proposed in the pre-Optane era. Since then, newer indexes based on real PM have appeared, but it is unclear how they compare to each other and to previous proposals, and what further challenges remain. This paper addresses these issues by analyzing and experimentally evaluating state-of-the-art PM range indexes built for real PM. We find that newer designs inherited past techniques with new improvements, but do not necessarily outperform pre-Optane era proposals. Moreover, PM indexes are often very competitive with or even outperform indexes tailored for DRAM, highlighting the potential of using a unified design for both PM and DRAM. Functionality-wise, these indexes still lack good support for variable-length keys and handling NUMA effect. Based on our findings, we distill new design principles and highlight future directions.

Persistent memory (PM) based indexing techniques have been proposed to build fast yet persistent indexes that sit on the memory bus. Over the past decade, numerous techniques have been proposed with various assumptions and different properties (e.g., some of them were proposed before real PM became available), making it hard for researchers and practitioners to gain a comprehensive understanding of the area. In this tutorial, we give a comprehensive overview of PM indexing techniques, covering both range and hash indexes. We contrast the designs proposed before and after real PM became available, summarize the common and useful design techniques, and discuss potential future challenges and opportunities in this area.

This paper introduces a robust fingerprint matching scheme based on the comprehensive minutia and the binary relation between minutiae. In the method, a fingerprint is represented as a graph, of which the comprehensive minutiae act as the vertex set and the local binary minutia relations provide the edge set. Then, the transformation-invariant and transformation-variant features are extracted from the binary relation. The transformation-invariant features are suitable to estimate the local matching probability, whereas the transformation-variant features are used to model the fingerprint rotation transformation with the adaptive Parzen window. Finally, the fingerprint matching is conducted with the variable bounded box method and iterative strategy. The experiments demonstrate that the proposed scheme is effective and robust in fingerprint alignment and matching.

This paper introduces a novel algorithm based on global comprehensive similarity with three steps. To describe the Euclidean space-based relative features among minutiae, we first build a minutia-simplex that contains a pair of minutiae as well as their associated textures, with its transformation-variant and invariant relative features employed for the comprehensive similarity measurement and parameter estimation, respectively. By the second step, we use the ridge-based nearest neighborhood among minutiae to represent the ridge-based relative features among minutiae. With these ridge-based relative features, minutiae are grouped according to their affinity with a ridge. The Euclidean space-based and ridge-based relative features among minutiae reinforce each other in the representation of a fingerprint. Finally, we model the relationship between transformation and the comprehensive similarity between two fingerprints in terms of histogram for initial parameter estimation. Through these steps, our experiment shows that the method mentioned above is both effective and suitable for limited memory AFIS owing to its less than 1k byte template size.

A biometric information processing toolkit (BITK) is designed and implemented to support the biometrics research, e.g. algorithm development and performance evaluation. BITK is an object-oriented C++ software development toolkit (SDK), and it provides a consistent, flexible and reusable framework to integrate algorithms, data structures, and visualization methods. In addition, an application platform based on BITK (BITKAPP) is developed. BITKAPP makes the best of BITK to serve the biometrics researchers with its friendly user interface and the plug-in architecture. The meaningful applications of the toolkit and platform confirm that they effectively support the biometrics research.

Abstract This paper introduces a fingerprint identification algorithm by clustering similarity with the view to overcome the dilemmas encountered in fingerprint identification. To decrease multi-spectrum noises in a fingerprint, we first use a dyadic scale space (DSS) method for image enhancement. The second step describes the relative features among minutiae by building a minutia-simplex which contains a pair of minutiae and their local associated ridge information, with its transformation-variant and invariant relative features applied for comprehensive similarity measurement and for parameter estimation respectively. The clustering method is employed to estimate the transformation space. Finally, multi-resolution technique is used to find an optimal transformation model for getting the maximal mutual information between the input and the template features. The experimental results including the performance evaluation by the 2nd International Verification Competition in 2002 (FVC2002), over the four fingerprint databases of FVC2002 indicate that our method is promising in an automatic fingerprint identification system (AFIS).