Statistical Analysis with Missing Data

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.

Minutia Cylinder-Code: A New Representation and Matching Technique for Fingerprint Recognition

COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19's spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long/Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications.

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Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment

Password-based remote user authentication schemes are widely investigated, with recent research increasingly combining a user's biometrics with a password to design a remote user authentication scheme that enhances the level of the security However, these authentication schemes are designed for a single server environment and result in users needing to register many times when they want to access different application servers To solve this problem, in this paper we propose an anonymous multi-server authenticating key agreement scheme based on trust computing using smart cards, password, and biometrics Our scheme not only supports multi-server environments but also achieves many security requirements In addition, our scheme is a lightweight authentication scheme which only uses the nonce and a hash function From the subsequent analysis, the proposed scheme can be seen to resist several kinds of attacks, and to have more security properties than other comparable schemes

An anonymous multi-server authenticated key agreement scheme based on trust computing using smart cards and biometrics

This article addresses the online exact string matching problem which consists in finding all occurrences of a given pattern p in a text t. It is an extensively studied problem in computer science, mainly due to its direct applications to such diverse areas as text, image and signal processing, speech analysis and recognition, information retrieval, data compression, computational biology and chemistry.In the last decade more than 50 new algorithms have been proposed for the problem, which add up to a wide set of (almost 40) algorithms presented before 2000. In this article we review the string matching algorithms presented in the last decade and present experimental results in order to bring order among the dozens of articles published in this area.

The exact online string matching problem: A review of the most recent results

A systematic method for fingerprint ridge orientation estimation and image segmentation

Fingerprint matching based on global alignment of multiple reference minutiae

Two steps for fingerprint segmentation

Fingerprint minutiae are prevalently used in AFISs. The extraction of fingerprint minutiae is heavily affected by the quality of fingerprint images. This leads to the incorporation of a fingerprint enhancement module in AFIS to make the system robust with respect to the quality of input fingerprint images. Most of existing enhancement methods suffer from mainly two kinds of defects: (1) time consuming and thus unusable in time critical applications; and (2) blocky effect in the enhanced image. This paper follows Hong’s Gabor filter based enhancement scheme (IEEE Trans. PAMI, vol.20, no.8, pp. 777-789, 1998) but uses a circle support filter and tunes the filter’s frequency and size differently. This scheme can enhance the fingerprint image rapidly and overcome the blocky effect effectively and does improve the performance of minutiae detection.

Fingerprint Enhancement Using Circular Gabor Filter

Incremental learning has been widely addressed in machine learning literature to deal with tasks where the learning environment is steadily changing or training samples become available one after another over time. Support Vector Machine has been successfully used in pattern recognition and function estimation. In order to tackle with incremental learning problems with new features, an incremental feature learning algorithm based on Least Square Support Vector Machine is proposed in this paper. In this algorithm, features of newly joined samples contain two parts: already existing features and new features. Using historic structural parameters which are trained from the already existing features, the algorithm only trains the new features with Least Square Support Vector Machine. Experiments show that this algorithm has two outstanding properties. First, different kernel functions can be used for the already existing features and the new features according to the distribution of samples. Consequently, this algorithm is more suitable to deal with classification tasks which can not be well solved by using a single kernel function. Second, the training time and the memory space can be reduced because the algorithm fully uses the structural parameters of classifiers trained formerly and only trains the new features with Least Square Support Vector Machine. Some UCI datasets are used to demonstrate the less training time and comparable or better performance of this algorithm than the Least Square Support Vector Machine.

Guomin Zhang

Papers

A systematic method for fingerprint ridge orientation estimation and image segmentation

Fingerprint matching based on global alignment of multiple reference minutiae

Two steps for fingerprint segmentation

Fingerprint Enhancement Using Circular Gabor Filter

An Incremental Feature Learning Algorithm Based on Least Square Support Vector Machine