Face Recognition Accuracy Improving Using Gray Level Co-occurrence Matrix Selection Feature Algorithm

Vera,; Kusnadi, Adhi; Pane, Ivransa Zuhdi; Overbeek, Marlinda Vasty; Prasetya, Syarief Gerald

doi:10.1109/icsca57840.2023.10087414

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…We built our object recognition system using these two unique powers of the fractal parameter. Also, in view of wide applications of GLCM in image classification problems (Salman et al, 2022;Vera et al, 2023;Alazawi et al 2019), GLCM feature was also computed to build the hybrid texture feature.…”

Section: Our Proposition For Cognition Based Texture Feature Extracti...mentioning

confidence: 99%

“…In order to prepare hybrid feature, other texture feature Gray-level Co-occurrence Matrix (GLCM) was explored in this work. Many researchers have used GLCM as a texture feature in facial recognition process (Salman et al, 2022;Vera et al, 2023;Alazawi et al 2019). Finally hybrid feature was prepared by combing ILMFD+GLCM in order to train the model effectively.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Approach for Object Recognition Using Decision Tree Clustering by Incorporating Multi-Level BPNN Classifiers and Hybrid Texture Features

Kumar

2024

International Journal of Information Retrieval Research

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This work proposes a novel approach to object recognition, particularly for human faces, based on the principle of human cognition. The suggested approach can handle a dataset or problem with a large number of classes for classification more effectively. The model for the facial recognition-based object detection system was constructed using a combination of decision tree clustering based multi-level Backpropagation neural network classifier-TFMLBPNN-DTC and hybrid texture feature (ILMFD+GLCM) and applied on NS and ORL databases. This model produced the classification accuracy (±standard deviation) of 95.37 ±0.951877% and 90.83 ± 1.374369% for single input and 96.58 ±0.5604582% and 91.50 ± 2.850439% for group-based decision for NS and ORL database respectively. The better classification results encourage its application to other object recognition and classification issues. This work's basic idea also makes it easier to improve classification management for a wide range of classes.

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Section: Our Proposition For Cognition Based Texture Feature Extracti...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Object Recognition Using Decision Tree Clustering by Incorporating Multi-Level BPNN Classifiers and Hybrid Texture Features

Kumar

2024

International Journal of Information Retrieval Research

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“…This method can moreover be improved by using preparatory information; thus, the more information utilized, the more superior the execution of the Neural Network. In any case, the Neural Network is restricted within a number of layers, although having more layers is advantageous, as the more layers there are, the higher capacity of the Neural Network [19]. Hence, to overcome this, Deep Learning was developed [5].…”

Section: Introductionmentioning

confidence: 99%

Small Timescaled Data for Covid-19 Prediction with RNN-LSTM in Tangerang Regency

Wijaya,

Overbeek

2023

IJAIDM

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Throughout the pandemic, many people have become familiarised with the new type of virus that has been spreading throughout the world, called the Coronavirus. On the 2nd of March, the year 2020, the Indonesian government had announced the identification of first Covid-19 case in Indonesia. With the arrival of Covid-19, and its spreading across all the provinces of Indonesia, the number of positive cases keeps growing even in the present day. Tangerang Regency is one of the areas that has opaqued citizens in the Banten Province. The purpose of this research is to discuss how to predict the sum of Covid-19 cases in the Tangerang Regency using the RNN-LSTM method. Although this method is very eloquent if used to perform a sequential task, its complexity and loss of gradient can make this model difficult to be trained, hence resulting in the use of the Long Short-Term Memory (LSTM) to reduce these weaknesses and help the RNN to look back on past data. This research uses Python as the programming language and Jupyter Notebook for the visualization of the results of the prediction. Therefore, the prediction model has been evaluated using various computational methods, such as RMSE with its error percentage of 0.05, and MSE and MAE with the same error percentage of 0.03 with the loss of their models being 9.6793e-04.

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Evaluation of machine learning models for mapping soil salinity in Ben Tre province, Vietnam

Khanh,

Ngoc,

Pramanik

2024

Multimed Tools Appl

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Face Recognition Accuracy Improving Using Gray Level Co-occurrence Matrix Selection Feature Algorithm

Cited by 7 publications

References 26 publications

A Novel Approach for Object Recognition Using Decision Tree Clustering by Incorporating Multi-Level BPNN Classifiers and Hybrid Texture Features

A Novel Approach for Object Recognition Using Decision Tree Clustering by Incorporating Multi-Level BPNN Classifiers and Hybrid Texture Features

Small Timescaled Data for Covid-19 Prediction with RNN-LSTM in Tangerang Regency

Evaluation of machine learning models for mapping soil salinity in Ben Tre province, Vietnam

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