In this paper we present a method to recognize human faces based on histograms of local orientation. Orientation histograms were used as input feature vectors for a k-nearest neigbour classifier. We present a method t...
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ISBN:
(纸本)3540379517
In this paper we present a method to recognize human faces based on histograms of local orientation. Orientation histograms were used as input feature vectors for a k-nearest neigbour classifier. We present a method to calculate orientation histograms of n x n subimages partitioning the 2D-camera image with the segmented face. Numerical experiments have been made utilizing the Olivetti Research Laboratory (ORL) database containing 400 images of 40 subjects. Remarkable recognition rates of 98% to 99% were achieved with this extremely simple approach.
We present in this paper a new facial feature localizer. It uses a kind of auto-associative neural network trained to localize specific facial features (like eyes and mouth corners) in orientation-free faces. One poss...
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ISBN:
(纸本)3540379517
We present in this paper a new facial feature localizer. It uses a kind of auto-associative neural network trained to localize specific facial features (like eyes and mouth corners) in orientation-free faces. One possible extension is presented where several specialized detectors are trained to deal with each face orientation. To select the best localization hypothesis, we combine radiometric and probabilistic information. The method is quite fast and accurate. The mean localization error (estimated on more than 700 test images) is lower than 9%.
In this paper, we present experiments comparing different training algorithms for Radial Basis Functions (RBF) neuralnetworks. In particular we compare the classical training which consist of an unsupervised training...
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ISBN:
(纸本)3540379517
In this paper, we present experiments comparing different training algorithms for Radial Basis Functions (RBF) neuralnetworks. In particular we compare the classical training which consist of an unsupervised training of centers followed by a supervised training of the weights at the output, with the full supervised training by gradient descent proposed recently in same papers. We conclude that a fully supervised training performs generally better. We also compare Batch training with Online training and we conclude that Online training suppose a reduction in the number of iterations.
Mathematical models are normally used to calculate the component concentrations in biological wastewater treatment. However, this work deals with the wastewater from a coke plant and it implies inhibition effects betw...
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ISBN:
(纸本)3540379517
Mathematical models are normally used to calculate the component concentrations in biological wastewater treatment. However, this work deals with the wastewater from a coke plant and it implies inhibition effects between components which do not permit the use of said mathematical models. Due to this, feed-forward neuralnetworks were used to estimate the ammonium concentration in the effluent stream of the biological plant. The architecture of the neural network is based on previous works in this topic. The methodology consists in performing a group of different sizes of the hidden layer and different subsets of input variables.
Automatic eye tracking is a challenging task, with numerous applications in biometrics, security, intelligent human-computer interfaces;and driver's sleepiness detection systems. Eye localization and extraction is...
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ISBN:
(纸本)3540379517
Automatic eye tracking is a challenging task, with numerous applications in biometrics, security, intelligent human-computer interfaces;and driver's sleepiness detection systems. Eye localization and extraction is, therefore, the first step to the solution of such problems. In this paper, we present a new method, based on neural autoassociators, to solve the problem of detecting eyes from a facial image. A subset of the AR Database, collecting individuals both with or without glasses and with open or closed eyes, has been used for experiments and benchmarking. Preliminary experimental results are very promising and demonstrate the efficiency of the proposed eye localization system.
neural network models for unsupervised patternrecognition learning are challenged when the difference between the patterns of the training set is small. The standard neural network architecture for pattern recognitio...
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ISBN:
(纸本)3540379517
neural network models for unsupervised patternrecognition learning are challenged when the difference between the patterns of the training set is small. The standard neural network architecture for patternrecognition learning consists of adaptive forward connections. and lateral inhibition, which provides competition between output neurons. We propose an additional adaptive inhibitory feedback mechanism, to emphasize the difference between training patterns and improve learning. We present an implementation of adaptive feedback inhibition for spiking neural network models, based on spike timing dependent plasticity (STDP). When the inhibitory feedback connections are adjusted using an anti-Hebbian learning rule, feedback inhibition suppresses the redundant activity of input units which code the overlap between similar stimuli. We show, that learning speed andpattern discriminatability can be increased by adding this mechanism to the standard architecture.
Estimation of probability density functions (pdf) is one major topic in patternrecognition. Parametric techniques rely on an arbitrary assumption on the form of the underlying, unknown distribution. Nonparametric tec...
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ISBN:
(纸本)3540379517
Estimation of probability density functions (pdf) is one major topic in patternrecognition. Parametric techniques rely on an arbitrary assumption on the form of the underlying, unknown distribution. Nonparametric techniques remove this assumption In particular, the Parzen Window (PW) relies on a combination of local window functions centered in the patterns of a training sample. Although effective, PW suffers from several limitations. artificialneuralnetworks (ANN) are, in principle, an alternative family of nonparametric models. ANNs are intensively used to estimate probabilities (e.g., class-posterior probabilities), but they have not been exploited so far to estimate pdfs. This paper introduces a simple neural-based algorithm for unsupervised, nonparametric estimation of pdfs, relying on PW. The approach overcomes the limitations of PW, possibly leading to improved pdf models. An experimental demonstration of the behavior of the algorithm w.r.t. PW is presented, using random samples drawn from a standard exponential pdf.
We explore the use of constraints with divisive hierarchical clustering. We mention some considerations on the effects of the inclusion of constraints into the hierarchical clustering process. Furthermore, we introduc...
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ISBN:
(纸本)3540379517
We explore the use of constraints with divisive hierarchical clustering. We mention some considerations on the effects of the inclusion of constraints into the hierarchical clustering process. Furthermore, we introduce an implementation of a semi-supervised divisive hierarchical clustering algorithm and show the influence of including constraints into the divisive hierarchical clustering process. In this task our main interest lies in building stable dendrograms when clustering with different subsets of data.
Hierarchical neuralnetworks show many benefits when employed for classification problems even when only simple methods analogous,to decision trees are used to retrieve the classification result. More complex ways of ...
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ISBN:
(纸本)3540379517
Hierarchical neuralnetworks show many benefits when employed for classification problems even when only simple methods analogous,to decision trees are used to retrieve the classification result. More complex ways of evaluating the hierarchy output that take into account the complete information the hierarchy provides yield improved classification results. Due to the hierarchical output space decomposition that is inherent to hierarchical neuralnetworks the usage of Dempster-Shafer evidence theory suggests itself as it allows for the representation of evidence at different levels of abstraction. Moreover, it provides the possibility to differentiate between uncertainty and ignorance. The proposed approach. has been evaluated using three different data sets and showed consistently improved classification results compared to the simple decision-tree-like retrieval method.
The two key factors to design an ensemble of neuralnetworks are how to train the individual networks and how to combine the different outputs to get a single output. In this paper we focus on the combination module. ...
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ISBN:
(纸本)3540379517
The two key factors to design an ensemble of neuralnetworks are how to train the individual networks and how to combine the different outputs to get a single output. In this paper we focus on the combination module. We have proposed two methods based on Stacked Generalization as the combination module of an ensemble of neuralnetworks. In this paper we have performed a comparison among the two versions of Stacked Generalization and six statistical combination methods in order to get the best combination method. We have used the mean increase of performance and the mean percentage or error reduction for the comparison. The results show that the methods based on Stacked Generalization are better than classical combiners.
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