Recognizing Arabic Sign Language gestures using depth sensors and a KSVM classifier

Almasre, Miada A.; Al-Nuaim, Hana

doi:10.1109/ceec.2016.7835904

Cited by 15 publications

(8 citation statements)

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“…Their research provided a true representation of ArSL when they increased the dataset sample from 224 to 1400 gestured letters [6]. Therefore, as the dataset was large, Computers 2017, 6, 20 3 of 13 they used the kernel support vector machine (KSVM) as the supervised learning algorithm, with the radial kernel set with two parameters [6].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, as the dataset was large, Computers 2017, 6, 20 3 of 13 they used the kernel support vector machine (KSVM) as the supervised learning algorithm, with the radial kernel set with two parameters [6]. In addition, to overcome the time complexity of interpreting data for their model, the authors used the principle component analysis (PCA) algorithm to simplify the large dataset by reducing features and deleting redundant, irrelevant, or erroneous data due to noise [6].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Four SVM Classifiers Used with Depth Sensors to Recognize Arabic Sign Language Words

Almasre

Al-Nuaim

2017

Computers

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Abstract:The objective of this research was to recognize the hand gestures of Arabic Sign Language (ArSL) words using two depth sensors. The researchers developed a model to examine 143 signs gestured by 10 users for 5 ArSL words (the dataset). The sensors captured depth images of the upper human body, from which 235 angles (features) were extracted for each joint and between each pair of bones. The dataset was divided into a training set (109 observations) and a testing set (34 observations). The support vector machine (SVM) classifier was set using different parameters on the gestured words' dataset to produce four SVM models, with linear kernel (SVMLD and SVMLT) and radial kernel (SVMRD and SVMRT) functions. The overall identification accuracy for the corresponding words in the training set for the SVMLD, SVMLT, SVMRD, and SVMRT models was 88.92%, 88.92%, 90.88%, and 90.884%, respectively. The accuracy from the testing set for SVMLD, SVMLT, SVMRD, and SVMRT was 97.059%, 97.059%, 94.118%, and 97.059%, respectively. Therefore, since the two kernels in the models were close in performance, it is far more efficient to use the less complex model (linear kernel) set with a default parameter.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of Four SVM Classifiers Used with Depth Sensors to Recognize Arabic Sign Language Words

Almasre

Al-Nuaim

2017

Computers

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“… SVM, which gave the highest accuracy results of ArSL letter classification in the experiments [13]  RF, which many researchers recommend for its high accuracy [36]  kNN, which is commonly used for its ease of interpretation and low processing time [25] The results of the three classifiers were combined, and results were reused as a new dataset to train the same classifiers. The result of this combination is called an -ensemble schema dataset.‖ Therefore, the training datasets were classified as an original dataset and an ensemble schema dataset.…”

Section: Classification Implementationmentioning

confidence: 99%

“…Although this research also used SVM to classify the 28 ArSL letters as in Al-Masre and Al-Nuaim [13], and to overcome the limitation of using the PCA algorithm, the proposed model focused on including all of the features of the collected data while adding a classification step, as recommended by the literature, to produce higher recognition accuracy. The extra step used the same classifiers that used the original dataset to classify the combined results (ensemble).…”

Section: Introductionmentioning

confidence: 99%

“…The research methodology of Al-Masre and Al-Nuaim for gesture recognition used only one classifier (SVM) as a supervised machine learning hand-gesturing model [13] to classify the 28 letters (considered classes) of the Arabic alphabet - Figure 1.‖ In addition, to overcome the time complexity of interpreting the data for their model, the researchers used the principle component analysis (PCA) algorithm to simplify the large dataset by reducing features. Recognition results were at 86% for the ArSL letters tested in their experiment [13].…”

Section: Introductionmentioning

confidence: 99%

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The Performance of Individual and Ensemble Classifiers for an Arabic Sign Language Recognition System

Almasre¹,

Al-Nuaim²

2017

ijacsa

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Abstract-The objective of this paper is to compare different classifiers' recognition accuracy for the 28 Arabic alphabet letters gestured by participants as Sign Language and captured by two depth sensors. The accuracy results of three individual classifiers: (1) the support vector machine (SVM), (2) random forest (RF), and (3) nearest neighbour (kNN), using the original gestured dataset were compared with the accuracy results using an ensemble of the results of each classifier, as recommended by the literature. SVM produced higher overall accuracy when running as an individual classifier regardless of the number of observations for each letter. However, for letters with fewer than 65 observations each, which created a far smaller dataset, RF had higher accuracy than SVM did when using the ensemble approach. Although RF produced higher accuracy results for classes with limited class observation data, the difference between the accuracy results of RF in phase 2 and SVM in phase 1 was negligible. The researchers conclude that such a difference does not warrant using the ensemble approach for this experiment, which adds more processing complexity without a significant increase in accuracy.

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A Survey on Hand Gesture Recognition Using Machine Learning and Infrared Information

Nogales

Benalcázar

2020

Communications in Computer and Information Science

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Recognizing Arabic Sign Language gestures using depth sensors and a KSVM classifier

Cited by 15 publications

References 23 publications

Comparison of Four SVM Classifiers Used with Depth Sensors to Recognize Arabic Sign Language Words

Comparison of Four SVM Classifiers Used with Depth Sensors to Recognize Arabic Sign Language Words

The Performance of Individual and Ensemble Classifiers for an Arabic Sign Language Recognition System

A Survey on Hand Gesture Recognition Using Machine Learning and Infrared Information

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