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Chai Z.,CAS Institute of Automation | Sun Z.,CAS Institute of Automation | Mendez-Vazquez H.,Advanced Technologies Application Center | He R.,CAS Institute of Automation | Tan T.,CAS Institute of Automation
IEEE Transactions on Information Forensics and Security | Year: 2014

Great progress has been achieved in face recognition in the last three decades. However, it is still challenging to characterize the identity related features in face images. This paper proposes a novel facial feature extraction method named Gabor ordinal measures (GOM), which integrates the distinctiveness of Gabor features and the robustness of ordinal measures as a promising solution to jointly handle inter-person similarity and intra-person variations in face images. In the proposal, different kinds of ordinal measures are derived from magnitude, phase, real, and imaginary components of Gabor images, respectively, and then are jointly encoded as visual primitives in local regions. The statistical distributions of these visual primitives in face image blocks are concatenated into a feature vector and linear discriminant analysis is further used to obtain a compact and discriminative feature representation. Finally, a two-stage cascade learning method and a greedy block selection method are used to train a strong classifier for face recognition. Extensive experiments on publicly available face image databases, such as FERET, AR, and large scale FRGC v2.0, demonstrate state-of-the-art face recognition performance of GOM. © 2005-2012 IEEE.


Munoz-Briseno A.,Advanced Technologies Application Center | Gago-Alonso A.,Advanced Technologies Application Center | Hernandez-Palancar J.,Advanced Technologies Application Center
Expert Systems with Applications | Year: 2013

Fingerprint indexing is a key technique in fingerprint identification systems. This strategy allows us to reduce the search space and the occurrences of false acceptance in databases with great size. This paper presents a new triplet based indexing algorithm which uses a new fingerprint representation, based on minutia triplets. This representation is an extension of the triangle set obtained from Delaunay triangulation. Also, a strategy is proposed in order to dismiss bad quality triplets that could affect the accuracy of the indexing process. This proposal shows a good accuracy, even when the fingerprints have bad quality areas. © 2012 Elsevier Ltd. All rights reserved.


Morales-Gonzalez A.,Advanced Technologies Application Center | Garcia-Reyes E.B.,Advanced Technologies Application Center
Multimedia Tools and Applications | Year: 2013

Spatial relations among objects and object parts play a fundamental role in the human perception and understanding of images, thus becoming very relevant in the computational fields of object recognition, scene understanding and contentbased image retrieval. In this work we propose a graph matching scheme that involves color, texture and shape features along with spatial descriptors to represent topological and orientation/directional relationships-which are obtained by means of combinatorial pyramids-in order to identify similar objects from a database. We also suggest a method for deciding which are the more useful levels in the hierarchy of segmentation for the recognition process. Our main objective is to prove that the combination of visual and spatial features is a promising road in order to improve the object recognition task. We performed experiments on two well known databases, COIL-100 and ETH-80 image sets, in order to evaluate the expressiveness of the proposed representation. These sets introduce challenges for simple object recognition in terms of view-point changes, and our results were comparable or superior than other state-of-the-art methods. © Springer Science+Business Media, LLC 2011.


Bande Serrano J.M.,Advanced Technologies Application Center | Palancar J.H.,Advanced Technologies Application Center
Computer Communications | Year: 2012

In this work, we propose a multi-character hardware-based solution using non-deterministic finite automata, NFA, for network intrusion detection. Our approach uses unique subsequence matching. This is a real-time preprocessing phase for detecting the possible presence and the corresponding alignment of the string in the data flow. In doing so, we make a reduction of the area cost for processing multiples characters. Instead of replicating the hardware by splitting the NFAs for each string alignment regarding the block of characters accepted at each cycle, we arrange the NFAs input so they match with the correct string alignment. The architecture is fully pipelined in order to reduce the latency. Taking four characters at the input we achieve multi gigabits throughputs, at the time that thousands of strings can be matched. © 2012 Elsevier B.V. All rights reserved.


Vega-Pons S.,Advanced Technologies Application Center | Ruiz-Shulcloper J.,Advanced Technologies Application Center
International Journal of Pattern Recognition and Artificial Intelligence | Year: 2011

Cluster ensemble has proved to be a good alternative when facing cluster analysis problems. It consists of generating a set of clusterings from the same dataset and combining them into a final clustering. The goal of this combination process is to improve the quality of individual data clusterings. Due to the increasing appearance of new methods, their promising results and the great number of applications, we consider that it is necessary to make a critical analysis of the existing techniques and future projections. This paper presents an overview of clustering ensemble methods that can be very useful for the community of clustering practitioners. The characteristics of several methods are discussed, which may help in the selection of the most appropriate one to solve a problem at hand. We also present a taxonomy of these techniques and illustrate some important applications. © 2011 World Scientific Publishing Company.


Acosta-Mendoza N.,Advanced Technologies Application Center | Gago-Alonso A.,Advanced Technologies Application Center | Medina-Pagola J.E.,Advanced Technologies Application Center
Knowledge-Based Systems | Year: 2012

The use of approximate graph matching for frequent subgraph mining has been identified in different applications as a need. To meet this need, several algorithms have been developed, but there are applications where it has not been used yet, for example image classification. In this paper, a new algorithm for mining frequent connected subgraphs over undirected and labeled graph collections VEAM (Vertex and Edge Approximate graph Miner) is presented. Slight variations of the data, keeping the topology of the graphs, are allowed in this algorithm. Approximate matching in existing algorithm (APGM) is only performed on vertex label set. In VEAM, the approximate matching between edge label set in frequent subgraph mining is included in the mining process. Also, a framework for graph-based image classification is introduced. The approximate method of VEAM was tested on an artificial image collection using a graph-based image representation proposed in this paper. The experimentation on this collection shows that our proposal gets better results than graph-based image classification using some algorithms reported in related work. © 2011 Elsevier B.V. All rights reserved.


Vega-Pons S.,Advanced Technologies Application Center | Correa-Morris J.,University of Habana | Ruiz-Shulcloper J.,Advanced Technologies Application Center
Pattern Recognition | Year: 2010

The combination of multiple clustering results (clustering ensemble) has emerged as an important procedure to improve the quality of clustering solutions. In this paper we propose a new cluster ensemble method based on kernel functions, which introduces the Partition Relevance Analysis step. This step has the goal of analyzing the set of partition in the cluster ensemble and extract valuable information that can improve the quality of the combination process. Besides, we propose a new similarity measure between partitions proving that it is a kernel function. A new consensus function is introduced using this similarity measure and based on the idea of finding the median partition. Related to this consensus function, some theoretical results that endorse the suitability of our methods are proven. Finally, we conduct a numerical experimentation to show the behavior of our method on several databases by making a comparison with simple clustering algorithms as well as to other cluster ensemble methods. © 2010 Elsevier Ltd. All rights reserved.


Correa-Morris J.,University of Habana | Espinosa-Isidron D.L.,Advanced Technologies Application Center | Alvarez-Nadiozhin D.R.,University of Habana
Pattern Recognition | Year: 2010

In many applied sciences the problem of revealing the underlying (crisp or fuzzy) structure (partitions or covers) in a collection of objects to be represented in non-temporal situations, measures, observations, phenomena, etc., is an essential task. Motivated by the independent use of some different partitions criteria and the theoretical and empirical analysis of some of its properties, in this paper, we introduce an incremental nested partition method which combines these partitions criteria for finding the inner structure of static and dynamic datasets. For this, we proved that there are relationships of nesting between partitions obtained, respectively, from these partition criteria, and besides that the sensitivity when a new object arrives to the dataset is rigorously studied. Our algorithm exploits all of these mathematical properties for obtaining the hierarchy of clusterings. Moreover, we realize a theoretical and experimental comparative study of our method with classical hierarchical clustering methods such as single-link and complete-link and other more recently introduced methods. The experimental results over databases of UCI repository and the AFP and TDT2 news collections show the usefulness and capability of our method to reveal different levels of information hidden in datasets. © 2010 Elsevier Ltd. All rights reserved.


Perez-Garcia O.A.,Advanced Technologies Application Center
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

Authentication is the verification of the identity of a person to access a resource or perform an activity. Authentication based on keystroke dynamics biometrics validates a legitimate user, comparing his typing on keyboard with his stored template. An important group of factors influences the capture of the raw data generated by the user’s typing. These Confounding Factors have been addressed in the literature from different approaches, and most of these studies agree that their influence affects the reliability of Keystroke Dynamics. In this research, a taxonomy of Confounding Factors is proposed, and several mitigation actions are discussed to face them. © Springer International Publishing Switzerland 2015.


Febrer-Hernandez J.K.,Advanced Technologies Application Center | Hernandez-Palancar J.,Advanced Technologies Application Center
Intelligent Data Analysis | Year: 2012

From the beginning of sequential pattern mining to the present, this field has received important attention within the data mining area, because it has a wide application in several significant computational problems. Many algorithms have been created and several techniques have been used with the objective of improving the discovery of the frequent sequence set. In this paper we present the main characteristics of some of the most important sequential pattern mining algorithms. Also, we show a comparative performance study among these algorithms. © 2012 - IOS Press and the authors. All rights reserved.

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