Dynamic Hand Gesture Recognition System for Kurdish Sign Language Using Two Lines of Features

Mahmood, Mayyadah Ramiz; Abdulazeez, Adnan Mohsin; Orman, Zeynep

doi:10.1109/icoase.2018.8548840

Cited by 19 publications

(19 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The much more prominently ranked features will be isolated from the others and used in four classifiers to determine which is the most accurate. Feature selection is a method of selecting features based on their ranking [30]. There are two types of fear used in this paper.…”

Section: Fase Detection and Feature Selectionmentioning

confidence: 99%

See 1 more Smart Citation

Performance evaluation of chi-square and relief-F feature selection for facial expression recognition

Mahmood

Abdulrazzaq

2022

IJEECS

Self Cite

View full text Add to dashboard Cite

Pattern recognition is a crucial part of machine learning that has recently piqued scientists' interest. The feature selection method utilized has an impact on the dataset's correctness and learning and training duration. Learning speed, comprehension and execution ease, and properly chosen features influence all high-quality outcomes. The two feature selection methods, relief-F and chi-square, are compared in this research. Each technique assesses and ranks attributes based on distinct criteria. Six of the most important features with the highest ranking have been chosen. The six features are utilized to compare the performance accuracy ratios of the four classifiers: k-nearest neighbor (KNN), naive Bayes (NB), multilayer perceptron (MLP), and random forests (RF) in terms of expression recognition. The final goal of the proposed strategy is to employ the least number of features from both feature selection methods to distinguish the four classifiers' accuracy performance. The proposed approach was trained and tested using the CK+ facial expression recognition dataset. According to the findings of the experiment, RF is the best accurate classifier on chi-square feature selection, with an accuracy of 94.23 %. According to a dataset utilized in this study, the relief-F feature selection approach had the best classifier, KNN, with an accuracy of 94.93 %

show abstract

Section: Fase Detection and Feature Selectionmentioning

confidence: 99%

“…MLP contains three layers: input, hidden, and output, each of which is completely functional [38]. The number of output nodes is equal to the number of classes [30]. Nodes in MLP can accomplish two tasks: initiating and aggregating.…”

Section: Multi Layer Perseptron (Mlp)mentioning

confidence: 99%

Performance evaluation of chi-square and relief-F feature selection for facial expression recognition

Mahmood

Abdulrazzaq

2022

IJEECS

Self Cite

View full text Add to dashboard Cite

show abstract

“…Pre-processing is mainly used in the preparation of images for enhanced identification and processing of features [29]. Preprocessing includes image acquisition as an input, grayscale acquisition, noise filtering, and binary image generation [30][31][32]. These techniques include an image histogram extension to better visibility and intensity adjustment that improves the picture's intensity values for an output picture to produce a high-quality picture and a histogram equalization that evenly distributes pixel intensities across the entire intensity range to increase the overall contrast [33].…”

Section: Preprocessing Skin Lesions Image Techniquesmentioning

confidence: 99%

Skin Lesions Classification Using Deep Learning Techniques: Review

Kareem

Abdulazeez

Zeebaree

2021

AJRCoS

View full text Add to dashboard Cite

Skin cancer is a significant health problem. More than 123,000 new cases per year are recorded. Melanoma is the most popular type of skin cancer, leading to more than 9000 deaths annually in the USA. Skin disease diagnosis is getting difficult due to visual similarities. While Melanoma is the most common form of skin cancer, other pathology types are also fatal. Automatic melanoma screening systems will be useful in identifying those skin cancers more appropriately. Advances in technology and growth in computational capabilities have allowed machine learning and deep learning algorithms to analyze skin lesion images. Deep Convolutional Neural Networks (DCNNs) have achieved more encouraging results, yet faster systems for diagnosing fatal diseases are the need of the hour. This paper presents a survey of techniques for skin cancer detection from images. The paper aims to present a review of existing state-of-the-art and effective models for automatically detecting Melanoma from skin images. The result of classifications and segmentation from the skin lesion images will be processed better using the ensemble deep learning algorithm.

show abstract

“…As a widely applied method, Viola-Jones [27] is considered as one of the most vigorous facedetection approaches because of its exceptional accuracy and robustness [28]. Add to that, it is especially applied for real time detection [29]. This method goes through four sequential steps of selection of Haar features which are extracted through complex computation to integral image creation, Adaboost are used to distinguish the face features from the rest, and cascading the sub windows that contain face image.…”

Section: Face Detectionmentioning

confidence: 99%

Classification techniques’ performance evaluation for facial expression recognition

Mahmood

Abdulrazaq

Zeebaree

et al. 2020

IJEECS

View full text Add to dashboard Cite

<p><span>Facial exprestion recognition as a recently developed method in computer vision is founded upon the idea of analazing the facial changes in which are witnessed due to emotional impacts on an individual. This paper provides a performance evaluation of a set of supervised classifiers used for facial expression recognition based on minimum features selected by chi-square. These features are the most iconic and influential ones that have tangible value for result dermination. The highest ranked six features are applied on six classifiers including multi-layer preceptron, support vector machine, decision tree, random forest, radial baised function, and k-nearest neioughbor to figure out the most accurate one when the minum number of features are utilized. This is done via analyzing and appraising the classifiers’ performance. CK+ is used as the research’s dataset. Random forest with the total accuracy ratio of 94.23 % is illustrated as the most accurate classifier amongst the rest. </span></p>

show abstract

Dynamic Hand Gesture Recognition System for Kurdish Sign Language Using Two Lines of Features

Cited by 19 publications

References 12 publications

Performance evaluation of chi-square and relief-F feature selection for facial expression recognition

Performance evaluation of chi-square and relief-F feature selection for facial expression recognition

Skin Lesions Classification Using Deep Learning Techniques: Review

Classification techniques’ performance evaluation for facial expression recognition

Contact Info

Product

Resources

About