Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

National Institute of Standards and Technology における超伝導研究及び生活

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Biometrics-based personal identification is regarded as an effective method for automatically recognizing, with a high confidence, a person's identity. This paper presents a new biometric approach to online personal identification using palmprint technology. In contrast to the existing methods, our online palmprint identification system employs low-resolution palmprint images to achieve effective personal identification. The system consists of two parts: a novel device for online palmprint image acquisition and an efficient algorithm for fast palmprint recognition. A robust image coordinate system is defined to facilitate image alignment for feature extraction. In addition, a 2D Gabor phase encoding scheme is proposed for palmprint feature extraction and representation. The experimental results demonstrate the feasibility of the proposed system.

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Online palmprint identification

A work in defining and implementing a biometric system based on hand geometry identification is presented here. Hand features are extracted from a color photograph taken when the user has placed his hand on a platform designed for such a task. Different pattern recognition techniques have been tested to be used in classification and/or verification from Euclidean distance to neural networks. Experimental results, up to a 97 percent rate of success in classification, will show the possibility of using this system in medium/high security environments with full acceptance from all users.

Biometric identification through hand geometry measurements

A biometric identification system, based on the processing of the human iris by the dyadic wavelet transform, has been introduced. The procedure to obtain an iris signature of 256 bits has been described, as well as the feature extraction block and the verification system. The results have shown that the system can achieve high rates of security.

Iris-based biometric recognition using dyadic wavelet transform

Two different approaches for iris recognition using Gabor filters and multiscale zero-crossing representation

In October 2000, after three years of competition between 15 candidate algorithms, the National Standards and Technology (NIST) chose the Rijndael algorithm to be adopted as Advanced Encryption Standard (AES) by the U.S. Department of Commerce, replacing to Data Encryption Algorithm (DES), which has been the standard since 1977. The authors analyze the structure and design of new AES, following three criteria: a) resistance against all known attacks; b) speed and code compactness on a wide range of platforms; and c) design simplicity; as well as its similarities and dissimilarities with other symmetric ciphers. On the other side, the principal advantages of new AES with respect to DES and T-DES, as well as its limitations, are investigated. Thus, for example, the fact that the new cipher and its inverse use different components, which practically eliminates the possibility for weak and semi-weak keys, as existing for DES, and the non-linearity of the key expansion, which practically eliminates the possibility of equivalent keys, are two of the principal advantages of new cipher. Finally, the implementation aspects of Rijndael cipher and its inverse are treated. Thus, although Rijndael is well suited to be implemented efficiently on a wide range of processors and in dedicated hardware, we have concentrated our study on 8-bit processors, typical for current smart cards and on 32-bit processors, typical for PCs.

The Rijndael block cipher (AES proposal) : a comparison with DES

A novel biometric identification approach based on the human iris pattern is proposed. The main idea of this technique is to represent the features of the iris by fine-to-coarse approximations at different resolution levels based on the discrete dyadic wavelet transform zero-crossing representation. The resulting one-dimensional (1D) signals are compared with model features using different distances. Before performing the feature extraction, a pre-processing step is to be made by image processing techniques, isolating the iris and enhancing the area of study. The proposed technique is translation, rotation and scale invariant. Results show a classification success above 98%, achieving an equal error rate equal to 0.21% and the possibility of having null false acceptance rates with low false rejection rates.

Carmen Sanchez-Avila

Papers

Biometric identification through hand geometry measurements

Iris-based biometric recognition using dyadic wavelet transform

Two different approaches for iris recognition using Gabor filters and multiscale zero-crossing representation

The Rijndael block cipher (AES proposal) : a comparison with DES

Iris recognition for biometric identification using dyadic wavelet transform zero-crossing