Face recognition with radial basis function (RBF) neural networks

Er, Meng Joo; Wu, Shiqian; Lu, Juwei; Toh, Hock Lye

doi:10.1109/tnn.2002.1000134

Cited by 519 publications

(57 citation statements)

References 43 publications

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“…The error rate of the NFL and CNN are 3.125 and 3.83%, respectively, whereas the error rate of the present method is 2.125%. Another method, face recognition with RBF neural network (Er et al 2002), has shown better performance than the present method (error rate is 1.92%) for the same ORL face database, but it involves huge computational effort due to the reason that it first extracts face features by PCA and then the resulting features are projected into the Fisher's optimal subspace and finally a hybrid learning algorithm is proposed to train the RBF neural networks for classification. Since the present method and method given in Er et al (2002) are using neural networks as classifier, comparison for computational cost is done for feature extraction only.…”

Section: Comparison With Other Methodsmentioning

confidence: 83%

“…Another method, face recognition with RBF neural network (Er et al 2002), has shown better performance than the present method (error rate is 1.92%) for the same ORL face database, but it involves huge computational effort due to the reason that it first extracts face features by PCA and then the resulting features are projected into the Fisher's optimal subspace and finally a hybrid learning algorithm is proposed to train the RBF neural networks for classification. Since the present method and method given in Er et al (2002) are using neural networks as classifier, comparison for computational cost is done for feature extraction only. The feature extraction for the method described in Er et al (2002) requires O(K(MN) 3 ) (Toygar and Acan 2004) to compute eigenvalues and the corresponding eigenvectors of the covariance matrix, which is constructed from K images of size M 9 N. Next to compute FLD, computational cost is O((MN) 3 ) .…”

Section: Comparison With Other Methodsmentioning

confidence: 83%

“…Since the present method and method given in Er et al (2002) are using neural networks as classifier, comparison for computational cost is done for feature extraction only. The feature extraction for the method described in Er et al (2002) requires O(K(MN) 3 ) (Toygar and Acan 2004) to compute eigenvalues and the corresponding eigenvectors of the covariance matrix, which is constructed from K images of size M 9 N. Next to compute FLD, computational cost is O((MN) 3 ) . Therefore, overall computational complexity is O((MN) 3 ) whereas the computational complexity of the present method to extract Gabor features is O((MN) 2 log (MN)).…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…The result of recognition on ORL face database using this method is very high, where error rate is only 2.625%. In Er et al (2002) the technique extracts face features first by PCA and then resulting features are projected into the Fisher's optimal subspace and finally, a hybrid learning algorithm is proposed to train the RBF neural networks achieved a high recognition rate. In a similar work, Er et al (2005) first the features are extracted using DCT and then Fisher's linear discriminant analysis (FLDA) has been used and finally RBF network classifies with an error rate of 2.45% for ORL face database.…”

Section: Related Workmentioning

confidence: 99%

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Human face recognition using fuzzy multilayer perceptron

et al. 2009

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In this work a novel method for human face recognition that is based on fuzzy neural network has been presented. Here, Gabor wavelet transformation is used for extraction of features from face images as it deals with images in spatial as well as in frequency domain to capture different local orientations and scales efficiently. In face recognition problem multilayer perceptron (MLP) has already been adopted owing to its efficiency, but it does not capture overlapping and nonlinear manifolds of faces which exhibit different variations in illumination, expression, pose, etc. A fuzzy MLP on the other hand performs better than an MLP because fuzzy MLP can identify decision surfaces in case of nonlinear overlapping classes, whereas an MLP is restricted to crisp boundaries only. In the present work, a new approach for fuzzification of the feature sets obtained through Gabor wavelet transforms has been discussed. The feature vectors thus obtained are classified using a newly designed fuzzified MLP. The system has been tested on a composite database (DB-C) consisting of the ORL face database and another face database created for this purpose and a recognition rate of 97.875% with fuzzy MLP against a recognition rate of only 81.25% with MLP whose feature vectors were also obtained through same Gabor wavelet transforms has been obtained.

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Section: Comparison With Other Methodsmentioning

confidence: 83%

Section: Comparison With Other Methodsmentioning

confidence: 83%

Section: Comparison With Other Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Human face recognition using fuzzy multilayer perceptron

et al. 2009

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“…Hai Guo and Jing-ying Zhao have proposed Chinese minority script recognition using radial basis function network [14]. Many researchers have implemented face recognition system using RBF neural network [15,16]. Bicheng Li and Hujun Yin have presented a face recognition system using radial basis function neural network and wavelet transformation [17].…”

Section: Introductionmentioning

confidence: 99%

Face Recognition Based on Wavelet Packet Coefficients and Radial Basis Function Neural Networks

Kathirvalavakumar¹,

Jeyasingh²

2013

JILSA

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An efficient face recognition system with face image representation using averaged wavelet packet coefficients, compact and meaningful feature vectors dimensional reduction and recognition using radial basis function (RBF) neural network is presented. The face images are decomposed by 2-level two-dimensional (2-D) wavelet packet transformation. The wavelet packet coefficients obtained from the wavelet packet transformation are averaged using two different proposed methods. In the first method, wavelet packet coefficients of individual samples of a class are averaged then decomposed. The wavelet packet coefficients of all the samples of a class are averaged in the second method. The averaged wavelet packet coefficients are recognized by a RBF network. The proposed work tested on three face databases such as Olivetti-Oracle Research Lab (ORL), Japanese Female Facial Expression (JAFFE) and Essex face database. The proposed methods result in dimensionality reduction, low computational complexity and provide better recognition rates. The computational complexity is low as the dimensionality of the input pattern is reduced.

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Feature extraction and recognition of UHF partial discharge signals in GIS based on dual‐tree complex wavelet transform

Tang

Zhou

2009

Int Trans Elec Energy Syst

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SUMMARYUltra-high-frequency (UHF) partial discharge (PD) detection is an effective means for evaluation of dielectric condition of gas insulated switchgear (GIS). Time-resolved data pattern is utilized to recognize insulation defect because of its attractive advantages that there exists some direct relationship between the shape of PD signal and the physics of the defect. Therefore, the main task is feature extraction of time-resolved data. However, it is hard to extract features in time domain because there is no obvious difference in types of signals. UHF PD signals are non-stationary, so feature extraction in frequency domain is not suitable as well. Feature extraction in time-frequency domain based on discrete wavelet transform (DWT) is a solution for this problem, but there is a weak point which hampers the application of this method, that is, lack of shift invariance, which means that small shifts in the input signal can cause major variations in the distribution of energy between DWT coefficients at different scales. In this paper, a new method of feature extraction of UHF PD signal on the basis of dual-tree complex wavelet transform (CWT) which can reduce shift sensitive is proposed. UHF PD signals are acquired in lab and decomposed by a dual-tree CWT. Then the feature space is constructed through the energy on all frequency bands and modulus maxima on all scales. At last feature space is inputted into a radial base function (RBF) neural network classifier. The results show the superiority of this method over traditional ones.

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Face recognition with radial basis function (RBF) neural networks

Cited by 519 publications

References 43 publications

Human face recognition using fuzzy multilayer perceptron

Human face recognition using fuzzy multilayer perceptron

Face Recognition Based on Wavelet Packet Coefficients and Radial Basis Function Neural Networks

Feature extraction and recognition of UHF partial discharge signals in GIS based on dual‐tree complex wavelet transform

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