Implementation of an rbf neural network on embedded systems: real-time face tracking and identity verification

Yang, Fan; Paindavoine, Michel

doi:10.1109/tnn.2003.816035

Cited by 143 publications

(22 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, utilization of neural networks [24] and neuro-fuzzy models [25] have been also introduced in literature for the problem of tracking. The benefits of these models include their high capability in mapping nonlinear relations and their generality of application.…”

Section: Related Workmentioning

confidence: 99%

Object tracking in videos by evolutionary clustering and locally linear neuro-fuzzy models

Saadian

2017

Teh. vjesn.

View full text Add to dashboard Cite

Original scientific paper In this paper a new method based on evolutionary clustering and locally linear neuro-fuzzy (LLNF) models is proposed for the problem of object tracking in videos. This approach utilizes clustering on color feature space to obtain a model of object which is given at the initial frame. To achieve the optimal clustering, evolutionary optimization methods are used. Based on the results of clustering, parameters of LLNF model is determined so it can be used as an identifier of object during the real time video streaming. To track the object, a swarm of weighted evolving linear models are used to estimate the location and size of the object at next frame based on its current and previous states. The performance of the proposed method is evaluated on a benchmark data set and compared to other methods performed on the same data set. The results show that the accuracy of the proposed method is superior to previous methods. Keywords: clustering; evolutionary computation; Locally Linear Neuro-Fuzzy model; object tracking; swarm optimization Praćenje predmeta u video snimkama pomoću modela evolucijskog grupiranja i lokalno linearnih neuro-fuzzy modelaIzvorni znanstveni članak U radu se za problem praćenja objekta u video snimkama predlaže nova metoda koja se zasniva na modelima evolucijskog grupiranja i lokalno linearnim neuro-fuzzy modelima (LLNF). Taj pristup koristi grupiranje na prostoru karakteristike u boji kako bi se dobio model predmeta zadanog u početnom okviru. Za postizanje optimalnog grupiranja, primijenjene su metode evolucijske optimizacije. Na temelju rezultata grupiranja određeni su parametri LLNF modela te se on može rabiti kao identifikator objekta tijekom video prijenosa u realnom vremenu. Za praćenje objekta koristi se roj ponderiranih izvedenih linearnih modela za procjenu lokacije i veličine objekta u sljedećem okviru na temelju njegovog postojećeg i prethodnih stanja. Učinkovitost predložene metode procijenjena je na referentnom nizu podataka i uspoređena s drugim metodama provedenim na istom nizu podataka. Rezultati pokazuju da je po točnosti predložena metoda bolja od prethodnih.

show abstract

Section: Related Workmentioning

confidence: 99%

Object tracking in videos by evolutionary clustering and locally linear neuro-fuzzy models

Saadian

2017

Teh. vjesn.

View full text Add to dashboard Cite

show abstract

“…Neural networks have also been used successfully for face recognition problem [9,[11][12][13][14][15]. The advantage of using the neural networks for face recognition is that the networks can be trained to capture more knowledge about the variation of face images and thereby achieving good generalization.…”

Section: Introductionmentioning

confidence: 99%

“…This has necessitated the search for an alternative model of the neural network for face recognition. In recent times, among the neural network approaches, many researchers have used radial basis function neural network (RBFNN) for face recognition [9,[11][12][13][14][15]. The RBF neural networks can be trained faster than MLP because of its locally tuned neurons [17] and has more compact topology compared to other models of neural networks [18].…”

Section: Introductionmentioning

confidence: 99%

“…In this method we have generated cluster centers from each individual of the database independently to capture more knowledge about distribution of facial images. Yang et al [13] have down-sampled the face images into 16 9 16 pixels and applied an RBFNN for recognition. Haddadnia et al [15] have combined the shape information and PCA to extract features from a face image and used in RBF neural networks for face recognition.…”

Section: Introductionmentioning

confidence: 99%

“…In the present study, the values of n and N are same and equals to the number of input patterns or training samples. d(x i , c j ) is the Euclidean distance between the input vector x i and the center vector c j and is usually considered as the dissimilarity measure, which is defined as follows [11,13]:…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

High-speed face recognition using self-adaptive radial basis function neural networks

Sing

Thakur

Basu

et al. 2009

Neural Comput & Applic

View full text Add to dashboard Cite

In this work, we have proposed a self-adaptive radial basis function neural network (RBFNN)-based method for high-speed recognition of human faces. It has been seen that the variations between the images of a person, under varying pose, facial expressions, illumination, etc., are quite high. Therefore, in face recognition problem to achieve high recognition rate, it is necessary to consider the structural information lying within these images in the classification process. In the present study, it has been realized by modeling each of the training images as a hidden layer neuron in the proposed RBFNN. Now, to classify a facial image, a confidence measure has been imposed on the outputs of the hidden layer neurons to reduce the influences of the images belonging to other classes. This process makes the RBFNN as self-adaptive for choosing a subset of the hidden layer neurons, which are in close neighborhood of the input image, to be considered for classifying the input image. The process reduces the computation time at the output layer of the RBFNN by neglecting the ineffective radial basis functions and makes the proposed method to recognize face images in high speed and also in interframe period of video. The performance of the proposed method has been evaluated on the basis of sensitivity and specificity on two popular face recognition databases, the ORL and the UMIST face databases. On the ORL database, the best average sensitivity (recognition) and specificity rates are found to be 97.30 and 99.94%, respectively using five samples per person in the training set. Whereas, on the UMIST database, the above quantities are found to be 96.36 and 99.81%, respectively using eight samples per person in the training set. The experimental results indicate that the proposed method outperforms some of the face recognition approaches.

show abstract

Fast Optoelectronic Neural Network for Vision Applications

Ruiz-Llata

Lamela

2005

Computational Intelligence and Bioinspired Systems

View full text Add to dashboard Cite

Implementation of an rbf neural network on embedded systems: real-time face tracking and identity verification

Cited by 143 publications

References 18 publications

Object tracking in videos by evolutionary clustering and locally linear neuro-fuzzy models

Object tracking in videos by evolutionary clustering and locally linear neuro-fuzzy models

High-speed face recognition using self-adaptive radial basis function neural networks

Fast Optoelectronic Neural Network for Vision Applications

Contact Info

Product

Resources

About