The Experts below are selected from a list of 6462 Experts worldwide ranked by ideXlab platform
Javier Ruizdelsolar - One of the best experts on this subject based on the ideXlab platform.
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illumination compensation and normalization in Eigenspace based face recognition a comparative study of different pre processing approaches
Pattern Recognition Letters, 2008Co-Authors: Javier Ruizdelsolar, Julio QuinterosAbstract:The aim of this work is to investigate illumination compensation and normalization in Eigenspace-based face recognition by carrying out an independent comparative study among several pre-processing algorithms. This research is motivated by the lack of direct and detailed comparisons of those algorithms in equal working conditions. The results of this comparative study intend to be a guide for the developers of face recognitions systems. The study focuses on algorithms with the following properties: (i) general purpose, (ii) no modeling steps or training images required, (iii) simplicity, (iv) high speed, and (v) high performance in terms of recognition rates. Thus, herein five different algorithms are compared, by using them as a pre-processing stage in 16 different Eigenspace-based face recognition systems. The comparative study is carried out in a face identification scenario using a large amount of images from the PIE, Yale B and Notre Dame face databases. As a result of this study we concluded that the most suitable algorithms for achieving illumination compensation and normalization in Eigenspace-based face recognition are SQI and the modified LBP transform.
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Eigenspace based face recognition a comparative study of different approaches
Systems Man and Cybernetics, 2005Co-Authors: Javier Ruizdelsolar, Pablo NavarreteAbstract:Eigenspace-based face recognition corresponds to one of the most successful methodologies for the computational recognition of faces in digital images. Starting with the Eigenface-Algorithm, different Eigenspace-based approaches for the recognition of faces have been proposed. They differ mostly in the kind of projection method used (standard, differential, or kernel Eigenspace), in the projection algorithm employed, in the use of simple or differential images before/after projection, and in the similarity matching criterion or classification method employed. The aim of this paper is to present an independent comparative study among some of the main Eigenspace-based approaches. We believe that carrying out independent studies is relevant, since comparisons are normally performed using the implementations of the research groups that have proposed each method, which does not consider completely equal working conditions for the algorithms. Very often, a contest between the abilities of the research groups rather than a comparison between methods is performed. This study considers theoretical aspects as well as simulations performed using the Yale Face Database, a database with few classes and several images per class, and FERET, a database with many classes and few images per class.
Seiji Ishikawa - One of the best experts on this subject based on the ideXlab platform.
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High-speed human motion recognition based on a motion history image and an Eigenspace
IEICE Transactions on Information and Systems, 2006Co-Authors: Takehito Ogata, Joo Kooi Tan, Seiji IshikawaAbstract:This paper proposes an efficient technique for human motion recognition\nbased on motion history images and an Eigenspace technique. In recent\nyears. human motion recognition has become one of the most popular\nresearch fields. It is expected to be applied in a security system,\nman-machine communication, and so on. In the proposed technique, we use\ntwo feature images and the Eigenspace technique to realize highspeed\nrecognition. An experiment was performed on recognizing six human\nmotions and the results showed satisfactory performance of the\ntechnique.
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human motion recognition using an Eigenspace
Pattern Recognition Letters, 2005Co-Authors: Masudur M Rahman, Seiji IshikawaAbstract:This paper describes a method for representing and/or recognizing human motion using an Eigenspace analysis, referred to a tuned Eigenspace, that is responsible for storing various human motions in terms of their sequential postures in an Eigenspace and for recognizing unfamiliar posture and/or motion from the tuned Eigenspace. We have also analyzed human dress texture problem, caused by wearing clothes, in the proposed method. The performed experiments include representation and recognition of (i) 6 actions of a cricket umpire played by 22 persons wearing respective dresses, and (ii) a turning motion given by a particular person wearing 10 typical clothes. The experimental results show a satisfactory performance of the proposed method for representing and recognizing human posture and/or motion overcoming the dress problem.
Pablo Navarrete - One of the best experts on this subject based on the ideXlab platform.
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Eigenspace based face recognition a comparative study of different approaches
Systems Man and Cybernetics, 2005Co-Authors: Javier Ruizdelsolar, Pablo NavarreteAbstract:Eigenspace-based face recognition corresponds to one of the most successful methodologies for the computational recognition of faces in digital images. Starting with the Eigenface-Algorithm, different Eigenspace-based approaches for the recognition of faces have been proposed. They differ mostly in the kind of projection method used (standard, differential, or kernel Eigenspace), in the projection algorithm employed, in the use of simple or differential images before/after projection, and in the similarity matching criterion or classification method employed. The aim of this paper is to present an independent comparative study among some of the main Eigenspace-based approaches. We believe that carrying out independent studies is relevant, since comparisons are normally performed using the implementations of the research groups that have proposed each method, which does not consider completely equal working conditions for the algorithms. Very often, a contest between the abilities of the research groups rather than a comparison between methods is performed. This study considers theoretical aspects as well as simulations performed using the Yale Face Database, a database with few classes and several images per class, and FERET, a database with many classes and few images per class.
Julio Quinteros - One of the best experts on this subject based on the ideXlab platform.
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illumination compensation and normalization in Eigenspace based face recognition a comparative study of different pre processing approaches
Pattern Recognition Letters, 2008Co-Authors: Javier Ruizdelsolar, Julio QuinterosAbstract:The aim of this work is to investigate illumination compensation and normalization in Eigenspace-based face recognition by carrying out an independent comparative study among several pre-processing algorithms. This research is motivated by the lack of direct and detailed comparisons of those algorithms in equal working conditions. The results of this comparative study intend to be a guide for the developers of face recognitions systems. The study focuses on algorithms with the following properties: (i) general purpose, (ii) no modeling steps or training images required, (iii) simplicity, (iv) high speed, and (v) high performance in terms of recognition rates. Thus, herein five different algorithms are compared, by using them as a pre-processing stage in 16 different Eigenspace-based face recognition systems. The comparative study is carried out in a face identification scenario using a large amount of images from the PIE, Yale B and Notre Dame face databases. As a result of this study we concluded that the most suitable algorithms for achieving illumination compensation and normalization in Eigenspace-based face recognition are SQI and the modified LBP transform.
Serhiy Kosinov - One of the best experts on this subject based on the ideXlab platform.
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an Eigenspace projection clustering method for inexact graph matching
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004Co-Authors: Terry Caelli, Serhiy KosinovAbstract:In this paper, we show how inexact graph matching (that is, the correspondence between sets of vertices of pairs of graphs) can be solved using the renormalization of projections of the vertices (as defined in this case by their connectivities) into the joint Eigenspace of a pair of graphs and a form of relational clustering. An important feature of this Eigenspace renormalization projection clustering (EPC) method is its ability to match graphs with different number of vertices. Shock graph-based shape matching is used to illustrate the model and a more objective method for evaluating the approach using random graphs is explored with encouraging results.
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inexact multisubgraph matching using graph Eigenspace and clustering models
Lecture Notes in Computer Science, 2002Co-Authors: Serhiy Kosinov, Terry CaelliAbstract:In this paper we show how inexact multisubgraph matching can be solved using methods based on the projections of vertices (and their connections) into the Eigenspaces of graphs - and associated clustering methods. Our analysis points to deficiencies of recent eigenspectra methods though demonstrates just how powerful full Eigenspace methods can be for providing filters for such computationally intense problems. Also presented are some applications of the proposed method to shape matching,in formation retrieval and natural language processing.
