Performance evaluation of different support vector machine kernels for face emotion recognition

Adeyanju, Ibrahim; Omidiora, E. O.; Oyedokun, Omobolaji F.

doi:10.1109/intellisys.2015.7361233

Cited by 27 publications

(15 citation statements)

References 7 publications

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“…Ibrahim Adeyanju et al [118] proposed a method in which he used four SVM kernels to classify different emotions of faces. They used a Radial Basis, Polynomial, Linear, and Quadratic functions as SVM kernels.…”

Section: ) Support Vector Machine (Svm)mentioning

confidence: 99%

Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges

et al. 2020

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With the advancements in machine and deep learning algorithms, the envision of various critical real-life applications in computer vision becomes possible. One of the applications is facial sentiment analysis. Deep learning has made facial expression recognition the most trending research fields in computer vision area. Recently, deep learning-based FER models have suffered from various technological issues like under-fitting or over-fitting. It is due to either insufficient training and expression data. Motivated from the above facts, this paper presents a systematic and comprehensive survey on current state-of-art Artificial Intelligence techniques (datasets and algorithms) that provide a solution to the aforementioned issues. It also presents a taxonomy of existing facial sentiment analysis strategies in brief. Then, this paper reviews the existing novel machine and deep learning networks proposed by researchers that are specifically designed for facial expression recognition based on static images and present their merits and demerits and summarized their approach. Finally, this paper also presents the open issues and research challenges for the design of a robust facial expression recognition system. INDEX TERMS Facial sentiment analysis, machine learning, deep learning, convolutional neural network, deep belief network, artificial intelligence.

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Section: ) Support Vector Machine (Svm)mentioning

confidence: 99%

Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges

et al. 2020

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“…In [15] 5499 digital camera and based on 51 persons, which includes 714 images of face emotion with seven facial expressions. The Quadratic kernel obtained best accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…where γ acts as a scaling parameter of the input data and c acts as a shifting parameter, which is supervise the mapping threshold (hence c =0) [15,16]. γ and c are the optimization parameters [30].…”

Section: Sigmoid Kernelmentioning

confidence: 99%

The effect of gamma value on support vector machine performance with different kernels

Al-Mejibli

Alwan

Hamed

2020

IJECE

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Currently, the support vector machine (SVM) regarded as one of supervised machine learning algorithm that provides analysis of data for classification and regression. This technique is implemented in many fields such as bioinformatics, face recognition, text and hypertext categorization, generalized predictive control and many other different areas. The performance of SVM is affected by some parameters, which are used in the training phase, and the settings of parameters can have a profound impact on the resulting engine’s implementation. This paper investigated the SVM performance based on value of gamma parameter with used kernels. It studied the impact of gamma value on (SVM) efficiency classifier using different kernels on various datasets descriptions. SVM classifier has been implemented by using Python. The kernel functions that have been investigated are polynomials, radial based function (RBF) and sigmoid. UC irvine machine learning repository is the source of all the used datasets. Generally, the results show uneven effect on the classification accuracy of three kernels on used datasets. The changing of the gamma value taking on consideration the used dataset influences polynomial and sigmoid kernels. While the performance of RBF kernel function is more stable with different values of gamma as its accuracy is slightly changed.

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“…For more than a decade, ML has been a powerful tool applied to all scientific fields, including speech recognition [14], translation between languages [17], emotion recognition [18], autonomous navigation of vehicles [19], product recommendations [20] and image processing [7,21]. Notably, there is a fast-growing trend of using ML algorithms in the health care industry [22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Machine learning and deep learning for clinical data and PET/SPECT imaging in Parkinson's disease: a review

2020

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Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that is increasingly applied to several medical diagnosis tasks, including a wide range of diseases. Importantly, various ML models were developed to address the complexity of Parkinson's Disease (PD) diagnosis. PD is a neurodegenerative disease characterized by motor and non-motor disorders where its syndromes affect the daily lives of patients. Several Computer Aided Diagnosis and Detection (CADD) systems based on hand-crafted ML algorithms achieved promising results in distinguishing PD patients from Healthy Control (HC) subjects and other Parkinsonian syndrome categories using clinical data (e.g., speech and gait impairments) and medical imaging [e.g., Position Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT)]. Despite the good performance of hand-crafted ML algorithms, there is still a problem linked to the features' extraction and selection. In fact, Deep Learning DL has provided an ultimate solution for the features' extraction and selection related issue. An important number of studies on the diagnosis of PD using DL algorithms were developed recently. This study provides an overview of the application of hand-crafted ML algorithms and DL techniques for PD diagnosis. It also introduces key concepts for understanding the application of ML methods to diagnose PD.

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Performance evaluation of different support vector machine kernels for face emotion recognition

Cited by 27 publications

References 7 publications

Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges

Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges

The effect of gamma value on support vector machine performance with different kernels

Machine learning and deep learning for clinical data and PET/SPECT imaging in Parkinson's disease: a review

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