Face Recognition Algorithm Using Two Dimensional Principal Component Analysis Based on Discrete Wavelet Transform

AlEnzi, Venus; Alfiras, Mohanad; Alsaqre, Falah

doi:10.1007/978-3-642-22389-1_38

Cited by 10 publications

(1 citation statement)

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“…AlEnzi et al apply different levels of discrete wavelet transform (DWT) in different levels and the two-dimensional principal component (2DPCA) to compute the face recognition accuracy processing it through the ORL image database [18]. From their experiments and combining 2-level DWL technique with 2DPCA method, the highest recognition accuracy (94.5%) was obtained with a time rate of 4.28 sec.…”

Section: Comparisons With Other Approachesmentioning

confidence: 99%

Face Recognition Using MLP and RBF Neural Network with Gabor and Discrete Wavelet Transform Characterization: A Comparative Study

Chelali

Djéradi

2015

Mathematical Problems in Engineering

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Face recognition has received a great attention from a lot of researchers in computer vision, pattern recognition, and human machine computer interfaces in recent years. Designing a face recognition system is a complex task due to the wide variety of illumination, pose, and facial expression. A lot of approaches have been developed to find the optimal space in which face feature descriptors are well distinguished and separated. Face representation using Gabor features and discrete wavelet has attracted considerable attention in computer vision and image processing. We describe in this paper a face recognition system using artificial neural networks like multilayer perceptron (MLP) and radial basis function (RBF) where Gabor and discrete wavelet based feature extraction methods are proposed for the extraction of features from facial images using two facial databases: the ORL and computer vision. Good recognition rate was obtained using Gabor and DWT parameterization with MLP classifier applied for computer vision dataset.

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Section: Comparisons With Other Approachesmentioning

confidence: 99%