Face recognition using 3D GLCM and Elman Levenberg recurrent Neural Network

Dillak, Rocky Yefrenes; Dana, Sumartini; Beily, Marthen

doi:10.1109/isemantic.2016.7873829

Cited by 7 publications

(3 citation statements)

References 11 publications

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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%

Liver Tumor Decision Support System on Human Magnetic Resonance Images: A Comparative Study

Alquran¹,

Al-Issa²,

Alslatie³

et al. 2023

Computer Systems Science and Engineering

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Liver cancer is the second leading cause of cancer death worldwide. Early tumor detection may help identify suitable treatment and increase the survival rate. Medical imaging is a non-invasive tool that can help uncover abnormalities in human organs. Magnetic Resonance Imaging (MRI), in particular, uses magnetic fields and radio waves to differentiate internal human organs tissue. However, the interpretation of medical images requires the subjective expertise of a radiologist and oncologist. Thus, building an automated diagnosis computer-based system can help specialists reduce incorrect diagnoses. This paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor images. This paper proposed two models; the first one employed the 3D features while the second exploited the 2D features. The first system uses 3D texture attributes, 3D shape features, and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor categories. On top of that, the proposed method is applied to 2D slices for comparison purposes. The proposed approach attained 100% accuracy in discriminating between all types of tumors, 100% Area Under the Curve (AUC), 100% sensitivity, and 100% specificity and precision as well in 3D liver tumors. On the other hand, the performance is lower in 2D classification. The maximum accuracy reached 96.4% for two classes and 92.1% for four classes. The top-class performance of the proposed system can be attributed to the exploitation of various types of feature selection methods besides utilizing the ReliefF features selection technique to choose the most relevant features associated with different classes. The novelty of this work appeared in building a highly accurate system under specific circumstances without any processing for the images and human input, besides comparing the performance between 2D and 3D classification. In the future, the presented work can be extended to be used

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Section: Texture Featuresmentioning

confidence: 99%

Liver Tumor Decision Support System on Human Magnetic Resonance Images: A Comparative Study

Alquran¹,

Al-Issa²,

Alslatie³

et al. 2023

Computer Systems Science and Engineering

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“…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%

3D pulmonary nodules detection using fast marching segmentation

Paing¹,

Choomchuay²

2018

J. Fundam and Appl Sci.

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Pulmonary nodule detection is an important step in lung cancer detection because nodules are the alert signal of lung cancer. The early detection of them can hence increase the patient's survival rates. This paper proposes an automated computer aided diagnosis system for detection of pulmonary nodules based on three dimensional (3D) structures. Lung parenchyma segmentation using fast marching method was employed. A simple thresholding technique is used to extract candidate nodules from the segmented lung parenchyma. A 3D image of nodule candidates is reconstructed by mean of stacked 2D images. To find the connected voxels of a blob, a 3D connected component labelling is used.

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“…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%

A Novel Statistical Feature Analysis-Based Global and Local Method for Face Recognition

Talab

Awang

Ansari³

2020

International Journal of Optics

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Face recognition from an image/video has been a fast-growing area in research community, and a sizeable number of face recognition techniques based on texture analysis have been developed in the past few years. Further, these techniques work well on gray-scale and colored images, but very few techniques deal with binary and low-resolution images. As the binary image is becoming the preferred format for low face resolution analysis, there is a need for further studies to provide a complete solution for the image-based face recognition system with a higher accuracy rate. To overcome the limitation of the existing methods in extracting distinctive features in low-resolution images due to the contrast between the face and background, we propose a statistical feature analysis technique to fill the gaps. To achieve this, the proposed technique integrates the binary-level occurrence matrix (BLCM) and the fuzzy local binary pattern (FLBP) named FBLCM to extract global and local features of the face from binary and low-resolution images. The purpose of FBLCM is to distinctively improve performance of edge sharpness between black and white pixels in the binary image and to extract significant data relating to the features of the face pattern. Experimental results on Yale and FEI datasets validate the superiority of the proposed technique over the other top-performing feature analysis methods. The developed technique has achieved the accuracy of 94.54% when a random forest classifier is used, hence outperforming other techniques such as the gray-level co-occurrence matrix (GLCM), bag of word (BOW), and fuzzy local binary pattern (FLBP), respectively.

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Face recognition using 3D GLCM and Elman Levenberg recurrent Neural Network

Cited by 7 publications

References 11 publications

Liver Tumor Decision Support System on Human Magnetic Resonance Images: A Comparative Study

Liver Tumor Decision Support System on Human Magnetic Resonance Images: A Comparative Study

3D pulmonary nodules detection using fast marching segmentation

A Novel Statistical Feature Analysis-Based Global and Local Method for Face Recognition

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