Raul Sanchez-Reillo

Bio: Raul Sanchez-Reillo is an academic researcher from Carlos III Health Institute. The author has contributed to research in topics: Biometrics & Mobile device. The author has an hindex of 20, co-authored 137 publications receiving 2057 citations. Previous affiliations of Raul Sanchez-Reillo include ETSI & Complutense University of Madrid.

Biometric identification through hand geometry measurements

Abstract: 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.

Iris-based biometric recognition using dyadic wavelet transform

Abstract: 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.

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

Abstract: Importance of biometric user identification is increasing everyday. One of the most promising techniques is the one based on the human iris. The authors, in this work, describe different approaches to develop this biometric technique. Based on the works carried out by Daugman, the authors have worked using Gabor filters and Hamming distance. But in addition, they have also worked in zero-crossing representation of the dyadic wavelet transform applied to two different iris signatures: one based on a single virtual circle of the iris; the other one based on an annular region. Also other metrics have been applied to be compared with the results obtained with the Hamming distance. In this work Euclidean distance and d Z will be shown. The last proposed approach is translation, rotation and scale invariant. Results will show a classification success up to 99.6% achieving an equal error rate down to 0.12 % and the possibility of having null false acceptance rates with very low false rejection rates.

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

Abstract: 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.

Iris Recognition with Low Template Size

Abstract: Among all the biometric techniques known nowadays, Iris Recognition is taken as the most promising of all, due to its low error rates without being invasive and with low relation to police records. Based on Daugman s work, the authors have developed their own Iris Recognition system, obtaining results that show the performance of the prototype and proves the excellences of the system initially developed by Daugman. A full coverage of the pre-processing and feature extraction blocks is given. Special efforts have been applied in order to obtain low template sizes and fast verification algorithms. This effort is intended to enable a human authentication in small embedded systems, such as an Integrated Circuit Card (smart cards). The final results show viability of this target, enabling a template size down to 256 bits. Future works will be focussed in new feature extraction algorithms, as well as optimising the pre-processing block.

[서평]「Applied Cryptography」

Abstract: 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.

IEEE transactions on pattern analysis and machine intelligence

Abstract: 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.

Online palmprint identification

Abstract: 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.

Biometrics: a tool for information security

Abstract: Establishing identity is becoming critical in our vastly interconnected society. Questions such as "Is she really who she claims to be?," "Is this person authorized to use this facility?," or "Is he in the watchlist posted by the government?" are routinely being posed in a variety of scenarios ranging from issuing a driver's license to gaining entry into a country. The need for reliable user authentication techniques has increased in the wake of heightened concerns about security and rapid advancements in networking, communication, and mobility. Biometrics, described as the science of recognizing an individual based on his or her physical or behavioral traits, is beginning to gain acceptance as a legitimate method for determining an individual's identity. Biometric systems have now been deployed in various commercial, civilian, and forensic applications as a means of establishing identity. In this paper, we provide an overview of biometrics and discuss some of the salient research issues that need to be addressed for making biometric technology an effective tool for providing information security. The primary contribution of this overview includes: 1) examining applications where biometric scan solve issues pertaining to information security; 2) enumerating the fundamental challenges encountered by biometric systems in real-world applications; and 3) discussing solutions to address the problems of scalability and security in large-scale authentication systems.

Personal identification based on iris texture analysis

Abstract: With an increasing emphasis on security, automated personal identification based on biometrics has been receiving extensive attention over the past decade. Iris recognition, as an emerging biometric recognition approach, is becoming a very active topic in both research and practical applications. In general, a typical iris recognition system includes iris imaging, iris liveness detection, and recognition. This paper focuses on the last issue and describes a new scheme for iris recognition from an image sequence. We first assess the quality of each image in the input sequence and select a clear iris image from such a sequence for subsequent recognition. A bank of spatial filters, whose kernels are suitable for iris recognition, is then used to capture local characteristics of the iris so as to produce discriminating texture features. Experimental results show that the proposed method has an encouraging performance. In particular, a comparative study of existing methods for iris recognition is conducted on an iris image database including 2,255 sequences from 213 subjects. Conclusions based on such a comparison using a nonparametric statistical method (the bootstrap) provide useful information for further research.