scholarly journals Detection of Iris Presentation Attacks Using Feature Fusion of Thepade’s Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7408
Author(s):  
Smita Khade ◽  
Shilpa Gite ◽  
Sudeep D. Thepade ◽  
Biswajeet Pradhan ◽  
Abdullah Alamri
Keyword(s):  
Contact Lenses ◽  
Feature Fusion ◽  
Local Level ◽  
Physical Space ◽  
Support Vector ◽  
Gray Level ◽  
Block Truncation Coding ◽  
Liveness Detection ◽  
Occurrence Matrix ◽  
Fusion Approach

Iris biometric detection provides contactless authentication, preventing the spread of COVID-19-like contagious diseases. However, these systems are prone to spoofing attacks attempted with the help of contact lenses, replayed video, and print attacks, making them vulnerable and unsafe. This paper proposes the iris liveness detection (ILD) method to mitigate spoofing attacks, taking global-level features of Thepade’s sorted block truncation coding (TSBTC) and local-level features of the gray-level co-occurrence matrix (GLCM) of the iris image. Thepade’s SBTC extracts global color texture content as features, and GLCM extracts local fine-texture details. The fusion of global and local content presentation may help distinguish between live and non-live iris samples. The fusion of Thepade’s SBTC with GLCM features is considered in experimental validations of the proposed method. The features are used to train nine assorted machine learning classifiers, including naïve Bayes (NB), decision tree (J48), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), and ensembles (SVM + RF + NB, SVM + RF + RT, RF + SVM + MLP, J48 + RF + MLP) for ILD. Accuracy, precision, recall, and F-measure are used to evaluate the performance of the projected ILD variants. The experimentation was carried out on four standard benchmark datasets, and our proposed model showed improved results with the feature fusion approach. The proposed fusion approach gave 99.68% accuracy using the RF + J48 + MLP ensemble of classifiers, immediately followed by the RF algorithm, which gave 95.57%. The better capability of iris liveness detection will improve human–computer interaction and security in the cyber-physical space by improving person validation.

scholarly journals Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 212
Author(s):  
Yajun Chen ◽  
Zhangnan Wu ◽  
Bo Zhao ◽  
Caixia Fan ◽  
Shuwei Shi
Keyword(s):  
Support Vector Machine ◽  
Image Segmentation ◽  
Feature Fusion ◽  
Connected Region ◽  
Support Vector ◽  
Gray Level ◽  
Rotation Invariant ◽  
Recognition Model ◽  
Corn Seedling ◽  
Occurrence Matrix

Detection of weeds and crops is the key step for precision spraying using the spraying herbicide robot and precise fertilization for the agriculture machine in the field. On the basis of k-mean clustering image segmentation using color information and connected region analysis, a method combining multi feature fusion and support vector machine (SVM) was proposed to identify and detect the position of corn seedlings and weeds, to reduce the harm of weeds on corn growth, and to achieve accurate fertilization, thereby realizing precise weeding or fertilizing. First, the image dataset for weed and corn seedling classification in the corn seedling stage was established. Second, many different features of corn seedlings and weeds were extracted, and dimensionality was reduced by principal component analysis, including the histogram of oriented gradient feature, rotation invariant local binary pattern (LBP) feature, Hu invariant moment feature, Gabor feature, gray level co-occurrence matrix, and gray level-gradient co-occurrence matrix. Then, the classifier training based on SVM was conducted to obtain the recognition model for corn seedlings and weeds. The comprehensive recognition performance of single feature or different fusion strategies for six features is compared and analyzed, and the optimal feature fusion strategy is obtained. Finally, by utilizing the actual corn seedling field images, the proposed weed and corn seedling detection method effect was tested. LAB color space and K-means clustering were used to achieve image segmentation. Connected component analysis was adopted to remove small objects. The previously trained recognition model was utilized to identify and label each connected region to identify and detect weeds and corn seedlings. The experimental results showed that the fusion feature combination of rotation invariant LBP feature and gray level-gradient co-occurrence matrix based on SVM classifier obtained the highest classification accuracy and accurately detected all kinds of weeds and corn seedlings. It provided information on weed and crop positions to the spraying herbicide robot for accurate spraying or to the precise fertilization machine for accurate fertilizing.

An enhanced method for the identification of ferritic morphologies in welded fusion zones based on gray-level co-occurrence matrix: A computational intelligence approach

2020 ◽  
pp. 095440622094226 ◽  
Author(s):  
Amanda Campos Souza ◽  
Gulliver Catão Silva ◽  
Lecino Caldeira ◽  
Fernando Marques de Almeida Nogueira ◽  
Moisés Luiz Lagares Junior ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Intelligence ◽  
Low Carbon Steel ◽  
Support Vector ◽  
Gray Level ◽  
Low Carbon ◽  
Identification Rate ◽  
Artificial Neural ◽  
Occurrence Matrix

This work focuses on the identification of five of the most common ferritic morphologies present in welded fusion zones of low carbon steel through images acquired by photomicrographies. With this regards, we discuss the importance of the gray-level co-occurrence matrix to extract the features to be used as the input of the computational intelligence techniques. We use artificial neural networks and support vector machines to identify the proportions of each morphology and present the error identification rate for each technique. The results show that the use of gray-level co-occurrence extraction allows a less intense computational model with statistical validity and the support vector machine as a computational intelligence technique allows smaller variability when compared to the artificial neural networks.

scholarly journals Perbandingan Metode Klasifikasi pada Pengolahan Citra Mata Ikan Tuna

2020 ◽  
Vol 5 ◽  
Author(s):  
Toni Dwi Novianto ◽  
I Made Susi Erawan
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Region Of Interest ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Gray Level ◽  
K Nearest Neighbors ◽  
Eye Color ◽  
Occurrence Matrix ◽  
Fish Eye

<p class="AbstractEnglish"><strong>Abstract:</strong> Fish eye color is an important attribute of fish quality. The change in eye color during the storage process correlates with freshness and has a direct effect on consumer perception. The process of changing the color of the fish eye can be analyzed using image processing. The purpose of this study was to obtain the best classification method for predicting fish freshness based on image processing in fish eyes. Three tuna fish were used in this study. The test was carried out for 20 hours with an eye image every 2 hours at room temperature. Fish eye image processing uses Matlab R.2017a software while the classification uses Weka 3.8 software. The image processing stages are taking fish eye image, segmenting ROI (region of interest), converting RGB image to grayscale, and feature extraction. Feature extraction used is the gray-level co-occurrence matrix (GLCM). The classification techniques used are artificial neural networks (ANN), k-neighborhood neighbors (k-NN), and support vector machines (SVM). The results showed the value using ANN = 0.53, k-NN = 0.83, and SVM = 0.69. Based on these results it can be determined that the best classification technique is to use the k-nearest neighbor (k-NN).</p><p class="AbstrakIndonesia"><strong>Abstrak:</strong> Warna mata ikan merupakan atribut penting pada kualitas ikan. Perubahan warna mata ikan selama proses penyimpanan berhubungan dengan tingkat kesegaran dan memiliki efek langsung pada persepsi konsumen. Proses perubahan warna mata ikan dapat dianalisis menggunakan pengolahan citra. Tujuan penelitian ini adalah mendapatkan metode klasifikasi terbaik untuk memprediksi kesegaran ikan berbasis pengolahan citra pada mata ikan. Tiga ekor ikan tuna digunakan dalam penelitian ini. Pengujian dilakukan selama 20 jam dengan pengambilan citra mata setiap 2 jam pada suhu ruang. Pengolahan citra mata ikan menggunakan software matlab R.2017a sedangkan pengklasifiannya menggunakan software Weka 3.8. Tahapan pengolahan citra meliputi pengambilan citra mata ikan, segmentasi ROI (<em>region of interest</em>), konversi citra RGB menjadi <em>grayscale</em>, dan ekstraksi fitur. Ekstraksi fitur yang digunakan yaitu <em>gray-level co-occurrence matrix</em> (GLCM).  Teknik klasifikasi yang digunakan yaitu, <em>artificial neural network</em> (ANN), <em>k-nearest neighbors</em> (k-NN), dan <em>support vector machine</em> (SVM). Hasil penelitian menunjukkan nilai korelasi menggunakan ANN = 0,53, k-NN = 0,83, dan SVM = 0,69. Berdasarkan hasil tersebut dapat disimpulkan teknik klasifikasi terbaik adalah menggunakan <em>k-nearest neighbors</em> (k-NN).</p>

scholarly journals Digit Classification of Majapahit Relic Inscription using GLCM-SVM

2018 ◽  
Vol 1 (2) ◽  
pp. 46
Author(s):  
Tri Septianto ◽  
Endang Setyati ◽  
Joan Santoso
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Template Matching ◽  
Support Vector ◽  
Gray Level ◽  
Class 1 ◽  
Occurrence Matrix ◽  
Handwritten Recognition

A higher level of image processing usually contains some kind of classification or recognition. Digit classification is an important subfield in handwritten recognition. Handwritten digits are characterized by large variations so template matching, in general, is inefficient and low in accuracy. In this paper, we propose the classification of the digit of the year of a relic inscription in the Kingdom of Majapahit using Support Vector Machine (SVM). This method is able to cope with very large feature dimensions and without reducing existing features extraction. While the method used for feature extraction using the Gray-Level Co-Occurrence Matrix (GLCM), special for texture analysis. This experiment is divided into 10 classification class, namely: class 1, 2, 3, 4, 5, 6, 7, 8, 9, and class 0. Each class is tested with 10 data so that the whole data testing are 100 data number year. The use of GLCM and SVM methods have obtained an average of classification results about 77 %.

scholarly journals LBP and GLCM Based Image Forgery Recognition

2018 ◽  
Vol 7 (4.6) ◽  
pp. 217
Author(s):  
D. Vaishnavi ◽  
T. S. Subashini ◽  
G. N. Balaji ◽  
D. Mahalakshmi
Keyword(s):  
Neural Network ◽  
Local Binary Pattern ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Gray Level ◽  
Image Splicing ◽  
Naked Eye ◽  
Occurrence Matrix

The forgery of digital images became very easy and it’s very difficult to ascertain the authenticity of such images by naked eye. Among the various kinds of image forgeries, image splicing is a frequent and widely used technique. Even though various methods are available to detect image splicing forgery, authors have attempted to provide a novel hybrid method which can yield greater accuracy, sensitivity and specificity. In this method, gray level co-occurrence matrix (GLCM) features are extracted using local binary pattern (LBP) operator on the image and the detection of the splicing forged images among the authentic images is done using the popular pattern recognition algorithms such as combined k-NN (Comb-KNN), back propagation neural network (BPNN) and support vector machine (SVM). The recorded results are also compared with the existing results of the previous studies to ascertain the quality of the results.  

scholarly journals Fingerprint Liveness Detection Based on Fine-Grained Feature Fusion for Intelligent Devices

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 517
Author(s):  
Xinting Li ◽  
Weijin Cheng ◽  
Chengsheng Yuan ◽  
Wei Gu ◽  
Baochen Yang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Feature Fusion ◽  
Texture Feature ◽  
Fusion Rule ◽  
Support Vector ◽  
Svm Classifier ◽  
Fine Grained ◽  
Liveness Detection ◽  
Fingerprint Liveness Detection ◽  
Intelligent Devices

Currently, intelligent devices with fingerprint identification are widely deployed in our daily life. However, they are vulnerable to attack by fake fingerprints made of special materials. To elevate the security of these intelligent devices, many fingerprint liveness detection (FLD) algorithms have been explored. In this paper, we propose a novel detection structure to discriminate genuine or fake fingerprints. First, to describe the subtle differences between them and take advantage of texture descriptors, three types of different fine-grained texture feature extraction algorithms are used. Next, we develop a feature fusion rule, including five operations, to better integrate the above features. Finally, those fused features are fed into a support vector machine (SVM) classifier for subsequent classification. Data analysis on three standard fingerprint datasets indicates that the performance of our method outperforms other FLD methods proposed in recent literature. Moreover, data analysis results of blind materials are also reported.

scholarly journals Analisis Kinerja Algoritma Mesin Pembelajaran untuk Klarifikasi Penyakit Stroke Menggunakan Citra CT Scan

2020 ◽  
Vol 7 (4) ◽  
pp. 833
Author(s):  
Nur Sakinah ◽  
Tessy Badriyah ◽  
Iwan Syarif
Keyword(s):  
Deep Learning ◽  
Ct Scan ◽  
Hemorrhagic Stroke ◽  
Image Data ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Gray Level ◽  
Stroke Patients ◽  
Occurrence Matrix ◽  
The Brain

<p>Stroke adalah suatu kondisi dimana pasokan darah ke otak terganggu sehingga bagian tubuh yang dikendalikan oleh area otak yang rusak tidak dapat berfungsi dengan baik. Penyebab stroke antara lain adalah terjadinya penyumbatan pada pembuluh darah (stroke iskemik) atau pecahnya pembuluh darah (stroke hemoragik). Pasien yang terkena stroke harus segera ditangani secepatnya karena sel otak dapat mati dalam hitungan menit. Tindakan penanganan stroke secara cepat dan tepat dapat mengurangi resiko kerusakan otak dan mencegah terjadinya komplikasi. Penelitian ini bertujuan untuk mengembangkan perangkat lunak yang dapat membaca dan menganalisis citra CT scan dari otak, dan kemudian secara otomatis memprediksi apakah citra CT scan tersebut stroke iskemik atau stroke hemoragik. Data citra CT scan berasal dari Rumah Sakit Umum Haji Surabaya yang diambil selama periode Januari-Mei 2019 dan berasal dari 102 pasien yang terindikasi stroke. Sebelum data gambar tersebut diolah dengan menggunakan beberapa algoritma mesin pembelajaran, data tersebut melalui tahap pre-processing yang bertujuan untuk meningkatkan kualitas citra meliputi konversi citra, pemotongan citra, penskalaan, greyscaling, penghilangan noise dan augmentasi. Tahap selanjutnya adalah ekstraksi fitur menggunakan metode Gray-Level Co-Occurrence Matrix (GLCM). Penelitian ini juga bertujuan untuk membandingkan kinerja lima algoritma mesin pembelajaran yaitu Naïve Bayes, Logistic Regression, Neural Network, Support Vector Machine dan Deep Learning yang diterapkan untuk memprediksi penyakit stroke. Hasil percobaan menunjukkan bahwa algoritma Deep Learning menghasilkan tingkat performansi paling tinggi yaitu nilai akurasi 96.78%, presisi 97.59% dan recall 95.92%.</p><p> </p><p><em><strong>Abstract</strong></em></p><p><em>Stroke is a condition in which the blood supply to the brain is interrupted so that parts of the body that are controlled by damaged brain areas cannot function properly. Causes of strokes include blockages in blood vessels (ischemic stroke) or rupture of blood vessels (hemorrhagic stroke). Stroke patients must be treated as soon as possible because brain cells can die within minutes. The handling of stroke patients quickly can reduce the risk of brain damage and prevent complications. This study aims to develop software that can read and analyze CT scan images from the brain, and then automatically predict whether the CT scan images are ischemic stroke or hemorrhagic stroke. The CT scan image data came from the Surabaya Hajj General Hospital which was taken during the January-May 2019 period and came from 102 patients who had indicated a stroke. Before the image data is processed using several machine learning algorithms, the data goes through a pre-processing phase which aims to improve image quality including image conversion, image cutting, scaling, greyscaling, noise removal and augmentation. The next step is feature extraction using the Gray-Level Co-Occurrence Matrix (GLCM) method. This study also aims to compare the performance of five machine learning algorithms, namely Naïve Bayes, Logistic Regression, Neural Networks, Support Vector Machines and Deep Learning which are applied to predict stroke. The experimental results show that the deep learning algorithm produces the highest level of performance where the accuracy value is 96.78%, 97.59% precision and 95.92% recall.</em></p><p><em><br /></em></p>

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