2016 International Seminar on Application for Technology of Information and Communication (ISemantic) 2016
DOI: 10.1109/isemantic.2016.7873829
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Face recognition using 3D GLCM and Elman Levenberg recurrent Neural Network

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Cited by 7 publications
(3 citation statements)
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“…Furthermore, Dacheng et al [29] used 3D co-occurrence matrices in Content-Based Image Retrieval (CBIR) applications, and Chen et al [30] used 3D-GLCM to extract the iris features for iris recognition and proved that The 3D-GLCM method could obtain a good recognition rate. Also, in [31], the characteristic features were extracted from face images using 3D GLCM matrices and obtained a high correct recognition rate (CRR). Moreover, Tan et al [32] proposed a 3D GLCM-based Convolution neural network (3D-GLCM CNN) model for the clinical task of polyp classification for discriminating volumetric malignant polyps from benign polyps.…”
Section: Texture Featuresmentioning
confidence: 99%
“…Furthermore, Dacheng et al [29] used 3D co-occurrence matrices in Content-Based Image Retrieval (CBIR) applications, and Chen et al [30] used 3D-GLCM to extract the iris features for iris recognition and proved that The 3D-GLCM method could obtain a good recognition rate. Also, in [31], the characteristic features were extracted from face images using 3D GLCM matrices and obtained a high correct recognition rate (CRR). Moreover, Tan et al [32] proposed a 3D GLCM-based Convolution neural network (3D-GLCM CNN) model for the clinical task of polyp classification for discriminating volumetric malignant polyps from benign polyps.…”
Section: Texture Featuresmentioning
confidence: 99%
“…Heterogeneously solid or ground glass Fig.9. Differences between nodules and non-nodules [26][27][28][29][30] 3D features of each blob are then extracted and fed into the classifier. Features are extracted to meet with the radiological features of pulmonary nodules.…”
Section: Segmentationmentioning
confidence: 99%
“…e methods include discrete wavelet transform (DWT, principal components analysis (PCA)) [22,23] and discrete cosine transform (DCT), and Gabor wavelet-based image provides the best resolution [24]. Other existing works also implemented the GLCM-based method to overcome the disadvantages [25][26][27].…”
Section: Introductionmentioning
confidence: 99%