Real-time face recognition with the continuous n-tuple classifier

Lucas, Sergio

doi:10.1049/ic:19980051

Cited by 10 publications

(9 citation statements)

References 2 publications

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“…Year Top-down HMM + gray tone features 13% [19] 1994 Eigenface 9.5% [22] 1994 Pseudo 2D HMM + gray tone features 5.5% [18] 1994 Elastic matching 20.0% [25] 1997 PDNN 4.0% [12] 1997 Continuous n-tuple classifier 2.7% [13] 1997 Top-down HMM + DCT coef.…”

Section: Methodsmentioning

confidence: 99%

Using hidden Markov models and wavelets for face recognition

Bicego

Castellani

Murino

12th International Conference on Image Analysis and Processing, 2003.Proceedings.

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In this paper, a new system for face recognition is proposed, based on Hidden Markov Models (HMMs) and wavelet coding. A sequence of overlapping sub-images is extracted from each face image, computing the wavelet coefficients for each of them. The whole sequence is then modelled by using Hidden Markov Models. The proposed method is compared with a DCT coefficients-based approach [9], showing comparable results. By using an accurate model selection procedure, we show that results proposed in [9] can be improved even more. The obtained results outperform all results presented in the literature on the Olivetti Research Laboratory (ORL) face database, reaching a ½¼¼± recognition rate. These performances proves the suitability of HMMs to deal with the new JPEG2000 image compression standard.

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Section: Methodsmentioning

confidence: 99%

Using hidden Markov models and wavelets for face recognition

Bicego

Castellani

Murino

12th International Conference on Image Analysis and Processing, 2003.Proceedings.

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“…In its second version, the core classification engine is based on a random n-tuple classifier (NNC) [16] and plastic self organizing maps (PSOM). It employs a pattern to pattern correlation algorithm called the normalized vector difference (NVD) algorithm.…”

Section: Digital Automated Identification System (Daisy)mentioning

confidence: 99%

Automated Insect Identification through Concatenated Histograms of Local Appearance Features

Larios

Deng

Zhang

et al. 2007

2007 IEEE Workshop on Applications of Computer Vision (WACV '07)

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This paper describes a computer vision approach to automated rapid-throughput taxonomic identification of stonefly larvae. The long-term goal of this research is to develop a cost-effective method for environmental monitoring based on automated identification of indicator species. Recognition of stonefly larvae is challenging because they are highly articulated, they exhibit a high degree of intraspecies variation in size and color, and some species are difficult to distinguish visually, despite prominent dorsal patterning. The stoneflies are imaged via an apparatus that manipulates the specimens into the field of view of a microscope so that images are obtained under highly repeatable conditions. The images are then classified through a process that involves (a) identification of regions of interest, (b) representation of those regions as SIFT vectors [1], (c) classification of the SIFT vectors into learned "features" to form a histogram of detected features, and (d) classification of the feature histogram via state-of-the-art ensemble classification algorithms. The steps (a) to (c) compose the concatenated feature histogram (CFH) method. We apply three region detectors for part (a) above, including a newly developed principal curvature-based region (PCBR) detector. This detector finds stable regions of high curvature via a watershed segmentation algorithm. We compute a separate dictionary of learned features for each region detector, and then concatenate the histograms prior to the final classification step.Send offprint requests to: Tom Dietterich, 1145 Kelley Engineering Center, Corvallis, OR 97331-5501, USA, tgd@eecs.oregonstate.eduWe evaluate this classification methodology on a task of discriminating among four stonefly taxa, two of which, Calineuria and Doroneuria, are difficult even for experts to discriminate. The results show that the combination of all three detectors gives four-class accuracy of 82% and three-class accuracy (pooling Calineuria and Doroneuria) of 95%. Each region detector makes a valuable contribution. In particular, our new PCBR detector is able to discriminate Calineuria and Doroneuria much better than the other detectors.

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“…DAISY is a CAT system that also has potential application in neuroinformatics, for instance in the automated screening of brain sections for speci¢c classes of neurons. DAISY is a general-purpose pattern recognition tool that uses a set of nearest-neighbour classi¢cation engines (Lucas 1997;Alexander & Stonham 1979) that run in parallel within a computational cluster environment. It is capable of identifying arbitrary pattern vectors by comparing them with training sets of pattern vectors, the identities of which are known.…”

Section: (D) Further Bioinformatic Applicationsmentioning

confidence: 99%

The portable UNIX programming system (PUPS) and CANTOR: a computational environment for dynamical representation and analysis of complex neurobiological data

O’Neill¹,

Hilgetag

2001

Phil. Trans. R. Soc. Lond. B

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Many problems in analytical biology, such as the classi¢cation of organisms, the modelling of macromolecules, or the structural analysis of metabolic or neural networks, involve complex relational data. Here, we describe a software environment, the portable UNIX programming system (PUPS), which has been developed to allow e¤cient computational representation and analysis of such data. The system can also be used as a general development tool for database and classi¢cation applications. As the complexity of analytical biology problems may lead to computation times of several days or weeks even on powerful computer hardware, the PUPS environment gives support for persistent computations by providing mechanisms for dynamic interaction and homeostatic protection of processes. Biological objects and their interrelations are also represented in a homeostatic way in PUPS. Object relationships are maintained and updated by the objects themselves, thus providing a £exible, scalable and current data representation. Based on the PUPS environment, we have developed an optimization package, CANTOR, which can be applied to a wide range of relational data and which has been employed in di¡erent analyses of neuroanatomical connectivity. The CANTOR package makes use of the PUPS system features by modifying candidate arrangements of objects within the system's database. This restructuring is carried out via optimization algorithms that are based on user-de¢ned cost functions, thus providing £exible and powerful tools for the structural analysis of the database content. The use of stochastic optimization also enables the CANTOR system to deal e¡ectively with incomplete and inconsistent data. Prototypical forms of PUPS and CANTOR have been coded and used successfully in the analysis of anatomical and functional mammalian brain connectivity, involving complex and inconsistent experimental data. In addition, PUPS has been used for solving multivariate engineering optimization problems and to implement the digital identi¢cation system (DAISY), a system for the automated classi¢cation of biological objects. PUPS is implemented in ANSI-C under the POSIX.1 standard and is to a great extent architecture-and operating-system independent. The software is supported by systems libraries that allow multi-threading (the concurrent processing of several database operations), as well as the distribution of the dynamic data objects and library operations over clusters of computers. These attributes make the system easily scalable, and in principle allow the representation and analysis of arbitrarily large sets of relational data. PUPS and CANTOR are freely distributed (http://www.pups.org.uk) as open-source software under the GNU license agreement.

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Real-time face recognition with the continuous n-tuple classifier

Cited by 10 publications

References 2 publications

Using hidden Markov models and wavelets for face recognition

Using hidden Markov models and wavelets for face recognition

Automated Insect Identification through Concatenated Histograms of Local Appearance Features

The portable UNIX programming system (PUPS) and CANTOR: a computational environment for dynamical representation and analysis of complex neurobiological data

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