An Introduction to MultiAgent Systems.

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

Biometric recognition offers a reliable solution to the problem of user authentication in identity management systems. With the widespread deployment of biometric systems in various applications, there are increasing concerns about the security and privacy of biometric technology. Public acceptance of biometrics technology will depend on the ability of system designers to demonstrate that these systems are robust, have low error rates, and are tamper proof. We present a high-level categorization of the various vulnerabilities of a biometric system and discuss countermeasures that have been proposed to address these vulnerabilities. In particular, we focus on biometric template security which is an important issue because, unlike passwords and tokens, compromised biometric templates cannot be revoked and reissued. Protecting the template is a challenging task due to intrauser variability in the acquired biometric traits. We present an overview of various biometric template protection schemes and discuss their advantages and limitations in terms of security, revocability, and impact on matching accuracy. A template protection scheme with provable security and acceptable recognition performance has thus far remained elusive. Development of such a scheme is crucial as biometric systems are beginning to proliferate into the core physical and information infrastructure of our society.

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Biometric template security

コンピュータ・サイエンス : ACM computing surveys

This paper presents recommended methodologies for the quantitative analysis of landslide hazard, vulnerability and risk at different spatial scales (site-specific, local, regional and national), as well as for the verification and validation of the results. The methodologies described focus on the evaluation of the probabilities of occurrence of different landslide types with certain characteristics. Methods used to determine the spatial distribution of landslide intensity, the characterisation of the elements at risk, the assessment of the potential degree of damage and the quantification of the vulnerability of the elements at risk, and those used to perform the quantitative risk analysis are also described. The paper is intended for use by scientists and practising engineers, geologists and other landslide experts.

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Recommendations for the quantitative analysis of landslide risk

The visual analysis of peripheral blood samples is an important test in the procedures for the diagnosis of leukemia. Automated systems based on artificial vision methods can speed up this operation and increase the accuracy and homogeneity of the response also in telemedicine applications. Unfortunately, there are not available public image datasets to test and compare such algorithms. In this paper, we propose a new public dataset of blood samples, specifically designed for the evaluation and the comparison of algorithms for segmentation and classification. For each image in the dataset, the classification of the cells is given, as well as a specific set of figures of merits to fairly compare the performances of different algorithms. This initiative aims to offer a new test tool to the image processing and pattern matching communities, direct to stimulating new studies in this important field of research.

All-IDB: The acute lymphoblastic leukemia image database for image processing

Deep-ECG: Convolutional Neural Networks for ECG biometric recognition

The classification and the count of white blood cells in microscopy images allows the in vivo assessment of a wide range of important hematic pathologies (i.e., from presence of infections to leukemia). Nowadays, the morphological cell classification is typically made by experienced operators. Such a procedure presents undesirable drawbacks: slowness and it presents a not standardized accuracy since it depends on the operator's capabilities and tiredness. Only few attempts of partial/full automated systems based on image-processing systems are present in literature and they are still at prototype stage. This paper presents a methodology to achieve an automated detection and classification of leucocytes by microscope color images. The proposed system firstly individuates in the blood image the leucocytes from the others blood cells, then it extracts morphological indexes and finally it classifies the leucocytes by a neural classifier in Basophil, Eosinophil, Lymphocyte, Monocyte and Neutrophil.

Morphological classification of blood leucocytes by microscope images

The early identification of acute lymphoblastic leukemia symptoms in patients can greatly increase the probability of recovery. Nowadays the leukemia disease can be identified by automatic specific tests such as Cytogenetics and Immunophenotyping and morphological cell classification made by experienced operators observing blood/marrow microscope images. Those methods are not included into large screening programs and are applied only when typical symptoms appears in normal blood analysis. The Cytogenetics and Immunophenotyping diagnostic methods are currently preferred for their great accuracy with respect to the method of blood cell observation which presents undesirable drawbacks: slowness and it presents a not standardized accuracy since it depends on the operator's capabilities and tiredness. Conversely, the morphological analysis just requires an image -not a blood sample- and hence is suitable for low-cost and remote diagnostic systems. The presented paper shows the effectiveness of an automatic morphological method to identify the Acute Lymphocytic Leukemia by peripheral blood microscope images. The proposed system firstly individuates in the blood image the leucocytes from the others blood cells, then it selects the lymphocyte cells (the ones interested by acute leukemia), it evaluates morphological indexes from those cells and finally it classifies the presence of the leukemia.

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Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images

We present a privacy preserving protocol for fingerprint-based authentication. We consider a scenario where a client equipped with a fingerprint reader is interested into learning if the acquired fingerprint belongs to the database of authorized entities managed by a server. For security, it is required that the client does not learn anything on the database and the server should not get any information about the requested biometry and the outcome of the matching process. The proposed protocol follows a multi-party computation approach and makes extensive use of homomorphic encryption as underlying cryptographic primitive. To keep the protocol complexity as low as possible, a particular representation of fingerprint images, named Fingercode, is adopted. Although the previous works on privacy-preserving biometric identification focus on selecting the best matching identity in the database, our main solution is a generic identification protocol and it allows to select and report all the enrolled identities whose distance to the user's fingercode is under a given threshold. Variants for simple authentication purposes are provided. Our protocols gain a notable bandwidth saving (about 25-39%) if compared with the best previous work (ICISC'09) and its computational complexity is still low and suitable for practical applications. Moreover, even if such protocols are presented in the context of a fingerprint-based system, they can be generalized to any biometric system that shares the same matching methodology, namely distance computation and thresholding.

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Fabio Scotti

Papers

All-IDB: The acute lymphoblastic leukemia image database for image processing

Deep-ECG: Convolutional Neural Networks for ECG biometric recognition

Morphological classification of blood leucocytes by microscope images

Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images

Privacy-preserving fingercode authentication