Feature Extraction Method GLCM and LVQ in Digital Image-Based Face Recognition

Sukiman, T. Sukma Achriadi; Suwilo, Saib; Zarlis, Muhammad

doi:10.33395/sinkron.v4i1.10199

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…To automatically identify gender, artificial intelligence, especially machine learning, is one of the tools often applied in human face recognition research [2]. The conclusion found in many studies in human face recognition using machine learning is that the feature extraction process on digital images cannot be said to be simple [3]. Transfer learning using a pre-trained model is one alternative solution to tackle this problem, especially using a Convolutional Neural Network (CNN) model [4].…”

Section: Imentioning

confidence: 99%

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Transfer Learning for Feral Cat Classification Using Logistic Regression

Tambunan¹,

Rosnelly

Situmorang³

2023

ICoSTEC

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Machine learning is an alternative tool for classifying animal species, especially feral cats. In this research, we use a machine learning algorithm to classify three species of feral cats: American Wildcat, Black-footed Cat, and European Wildcat. We also use a transfer learning model using the VGG-19 network for extracting the features in the feral cat images. By combining the VGG-19 and logistic regression algorithm, we build six models and compare which one is the best to solve the problem. We evaluate and analyze all models using a 5-fold, 10-fold, and 20- fold cross-validation, with accuracy, precision, and recall as the base performance value. The best result obtained is a model with a lasso regularization and cost parameter value of 1, with an accuracy value of 0.846667, a precision value of 0.845389, and a recall value of 0.846667. We also tune the C parameter in each LR model with values such as 0.1, 0.5, and 1. The most optimum C value for the lasso and ridge regularization is one, resulting in an average value of accuracy = 0.813, precision = 0.812, and recall = 0.813.

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Section: Imentioning

confidence: 99%

“…The sigmoid kernel function is a kernel function that is also often used in classification using the SVM algorithm, where this kernel has hyperparameters γ (input data scale parameter) and c (mapping threshold shift parameter). Equation (3) shows how to use these hyperparameters in a sigmoid kernel function [21]:…”

Section: A Support Vector Machinementioning

confidence: 99%

Transfer Learning for Feral Cat Classification Using Logistic Regression

Tambunan¹,

Rosnelly

Situmorang³

2023

ICoSTEC

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“…Six features will be generated from the GLCM feature extraction process. The following are six features used in this study [9]:…”

Section: Gray Level Co-occurrence Matrixmentioning

confidence: 99%

Modified KNN-LVQ for Stairs Down Detection Based on Digital Image

Johan

Putri

Hajar

et al. 2021

LKJITI

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Persons with visual impairments need a tool that can detect obstacles around them. The obstacles that exist can endanger their activities. The obstacle that is quite dangerous for the visually impaired is the stairs down. The stairs down can cause accidents for blind people if they are not aware of their existence. Therefore we need a system that can identify the presence of stairs down. This study uses digital image processing technology in recognizing the stairs down. Digital images are used as input objects which will be extracted using the Gray Level Co-occurrence Matrix method and then classified using the KNN-LVQ hybrid method. The proposed algorithm is tested to determine the accuracy and computational speed obtained. Hybrid KNN-LVQ gets an accuracy of 95%. While the average computing speed obtained is 0.07248 (s).

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“…Pada penelitian tersebut dapat melakukan pengenalan wajah dengan akurasi tertinggi hingga 96,56%. Penelitian lainnya mengenai penerapan jaringan saraf tiruan LVQ untuk pengenalan wajah yaitu penelitian yang dilakukan oleh (Sukiman, et al, 2019), meneliti tentang penggunaan model jaringan LVQ dan GLCM (gray level co-occurrence matrix) untuk pengenalan wajah. Dari perolehan hasil pengenalan wajah tersebut mendapatkan tingkat akurasi sebesar 90%.…”

Section: Pendahuluanunclassified

Kombinasi Jaringan Learning Vector Quantization Dan Normalized Cross Correlation Pada Pengenalan Wajah

Saleh¹,

Indra²,

Harahap³

2020

JUSIKOM

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Pengenalan wajah merupakan cara yang dilakukan untuk mengidentifikasi wajah berdasarkan nilai ciri yang terdapat pada citra wajah dan dapat diterapkan di dalam berbagai sistem, seperti absensi, akses keamanan ruangan dan login aplikasi atau perangkat. Salah satu algoritma untuk pengenalan wajah adalah LVQ (learning vector quantization), tetapi dalam pemilihan bobot awal yang kurang tepat dapat berdampak pada penurunan kinerja algoritma tersebut, sehingga hasil dari pengenalan wajah kurang akurat. Permasalahan ini dapat diselesaikan dengan penentuan bobot awal yang tepat dengan metode tertentu. Bobot yang dipilih pada penelitian ini berdasarkan kemiripan citra, salah satu metode untuk mengukur kemiripan adalah NCC (Normalized Cross Correlation). Penelitian ini akan dilakukan dengan mengkombinasi jaringan LVQ dengan menggunakan NCC dalam penentuan bobot awal untuk pengenalan wajah. Hasil pengujian yang diperoleh dengan kombinasi kedua metode tersebut untuk pengenalan wajah sebesar 94%.

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Feature Extraction Method GLCM and LVQ in Digital Image-Based Face Recognition

Cited by 7 publications

References 5 publications

Transfer Learning for Feral Cat Classification Using Logistic Regression

Transfer Learning for Feral Cat Classification Using Logistic Regression

Modified KNN-LVQ for Stairs Down Detection Based on Digital Image

Kombinasi Jaringan Learning Vector Quantization Dan Normalized Cross Correlation Pada Pengenalan Wajah

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