Unsupervised Learning Approach-Based New Optimization K-Means Clustering for Finger Vein Image Localization

Sulaiman, Dawlat Mustafa; Abdulazeez, Adnan Mohsin; Haron, Habibollah; Sadiq, Shereen Saleem

doi:10.1109/icoase.2019.8723749

Cited by 26 publications

(18 citation statements)

References 15 publications

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“…The transformation method is done by algebraic transformation, and according to some optimization criteria [39,40]. Also, feature extraction has the ability to handle essential information during dealing with high dimensional issues [41,42]. These dimensionality reduction techniques aim to not lose a large amount of information during the feature transformation process by conserving the original relative distance between features and cover the original data potential structure [10].…”

Section: Feature Extractionmentioning

confidence: 99%

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Zebari¹,

Abdulazeez²,

Zeebaree³

et al. 2020

JASTT

545

210

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Due to sharp increases in data dimensions, working on every data mining or machine learning (ML) task requires more efficient techniques to get the desired results. Therefore, in recent years, researchers have proposed and developed many methods and techniques to reduce the high dimensions of data and to attain the required accuracy. To ameliorate the accuracy of learning features as well as to decrease the training time dimensionality reduction is used as a pre-processing step, which can eliminate irrelevant data, noise, and redundant features. Dimensionality reduction (DR) has been performed based on two main methods, which are feature selection (FS) and feature extraction (FE). FS is considered an important method because data is generated continuously at an ever-increasing rate; some serious dimensionality problems can be reduced with this method, such as decreasing redundancy effectively, eliminating irrelevant data, and ameliorating result comprehensibility. Moreover, FE transacts with the problem of finding the most distinctive, informative, and decreased set of features to ameliorate the efficiency of both the processing and storage of data. This paper offers a comprehensive approach to FS and FE in the scope of DR. Moreover, the details of each paper, such as used algorithms/approaches, datasets, classifiers, and achieved results are comprehensively analyzed and summarized. Besides, a systematic discussion of all of the reviewed methods to highlight authors' trends, determining the method(s) has been done, which significantly reduced computational time, and selecting the most accurate classifiers. As a result, the different types of both methods have been discussed and analyzed the findings.

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Section: Feature Extractionmentioning

confidence: 99%

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Zebari¹,

Abdulazeez²,

Zeebaree³

et al. 2020

JASTT

545

210

View full text Add to dashboard Cite

show abstract

“…Nowadays, generative adversarial networks are largely investigated; the results showed that GANs could create highly realistic images from scratch [49], [50]. One of the major difficulties in learning facial beauty is that it must be carried out in an unsupervised manner because there are no fewer or more pairs of attractive images of an equal individual that may require supervised learning [51]. Diamant et al [37] created face images based on the condition of their beauty score.…”

Section: Bfp and Generative Adversarial Network (Gans)mentioning

confidence: 99%

Facial Beauty Prediction and Analysis based on Deep Convolutional Neural Network: A Review

Saeed

Abdulazeez

2021

JSCDM

Self Cite

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Facial attractiveness or facial beauty prediction (FBP) is a current study that has several potential usages. It is a key difficulty area in the computer vision domain because of the few public databases related to FBP and its experimental trials on the minor-scale database. Moreover, the evaluation of facial beauty is personalized in nature, with people having personalized favor of beauty. Deep learning techniques have displayed a significant ability in terms of analysis and feature representation. The previous studies focussed on scattered portions of facial beauty with fewer comparisons between diverse techniques. Thus, this article reviewed the recent research on computer prediction and analysis of face beauty based on deep convolution neural network DCNN. Furthermore, the provided possible lines of research and challenges in this article can help researchers in advancing the state – of- art in future work.

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“…Next, an image input RGB of m×n size is read and converted into a gray image with the standard equation [24]. The circumference of the face was detected with Haar [25], [26], [27] Cascade pictures library. Those rectangular facial expressions were then cut off and reported to the same scale.…”

Section: Facial Expression Recognition (Fer)mentioning

confidence: 99%

Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review

Abdullah

Abdulazeez

2021

JSCDM

Self Cite

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Facial emotional processing is one of the most important activities in effective calculations, engagement with people and computers, machine vision, video game testing, and consumer research. Facial expressions are a form of nonverbal communication, as they reveal a person's inner feelings and emotions. Extensive attention to Facial Expression Recognition (FER) has recently been received as facial expressions are considered. As the fastest communication medium of any kind of information. Facial expression recognition gives a better understanding of a person's thoughts or views and analyzes them with the currently trending deep learning methods. Accuracy rate sharply compared to traditional state-of-the-art systems. This article provides a brief overview of the different FER fields of application and publicly accessible databases used in FER and studies the latest and current reviews in FER using Convolution Neural Network (CNN) algorithms. Finally, it is observed that everyone reached good results, especially in terms of accuracy, with different rates, and using different data sets, which impacts the results.

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Unsupervised Learning Approach-Based New Optimization K-Means Clustering for Finger Vein Image Localization

Cited by 26 publications

References 15 publications

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Facial Beauty Prediction and Analysis based on Deep Convolutional Neural Network: A Review

Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review

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