A Classifier Model based on the Features Quantitative Analysis for Facial Expression Recognition

Jamshidnezhad, Amir; Nordin, Jan

doi:10.18517/ijaseit.1.4.81

Cited by 6 publications

(6 citation statements)

References 18 publications

(16 reference statements)

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“…The genetic algorithm can be used as a method to find an optimal hyperparameter of machine learning methods. For example in [25], Genetic Algorithm is used as hyperparameter tuning of a fuzzy rule to classify facial expression. The purpose is to make the fuzzy method to get a better classification result.…”

Section: ) Adaptive Boosting (Adaboost)mentioning

confidence: 99%

Hyper Parameter Optimization using Genetic Algorithm on Machine Learning Methods for Online News Popularity Prediction

Wicaksono¹,

Supianto²

2018

ijacsa

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Online news is a media for people to get new information. There are a lot of online news media out there and a many people will only read news that is interesting for them. This kind of news tends to be popular and will bring profit to the media owner. That's why, it is necessary to predict whether a news is popular or not by using the prediction methods. Machine learning is one of the popular prediction methods we can use. In order to make a higher accuracy of prediction, the best hyper parameter of machine learning methods need to be determined. Determining the hyper parameter can be time consuming if we use grid search method because grid search is a method which tries all possible combination of hyper parameter. This is a problem because we need a quicker time to make a prediction of online news popularity. Hence, genetic algorithm is proposed as the alternative solution because genetic algorithm can get optimal hypermeter with reasonable time. The result of implementation shows that genetic algorithm can get the hyper parameter with almost the same result with grid search with faster computational time. The reduction in computational time is as follows: Support Vector Machine is 425.06%, Random forest is 17%, Adaptive Boosting is 651.06%, and lastly K -Nearest Neighbour is 396.72%.

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Section: ) Adaptive Boosting (Adaboost)mentioning

confidence: 99%

Hyper Parameter Optimization using Genetic Algorithm on Machine Learning Methods for Online News Popularity Prediction

Wicaksono¹,

Supianto²

2018

ijacsa

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“…But in dynamic system, a multilevel HMM classifier is implemented which allows the automatic segmentation of arbitrary long sequence to different expression segments. Amir Jamshidnezhad and Md Jan Nordin [17] used quantitative analysis in order to find the most effective features movements between the selected facial feature points. In all these methods, features are extracted from the image first and then fed into a classifier and the output is one of the emotion categories.…”

Section: B Facial Expression Recognition Studiesmentioning

confidence: 99%

Multimodal Approach to Emotion Recognition for Enhancing Human Machine Interaction - A Survey

Veni

Thushara

2017

International Journal on Advanced Science, Engineering and Information Technology

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Emotions are defined as a mental state that occurs instinctively rather than through voluntary effort. They are strong feelings triggered by experiencing the joy, hate, fear, love and is followed by some physiological changes. Emotions play a vital role in social interactions and facilitate the decision making and perception in human being. Emotions are conveyed through speech, facial expression or by physiological signals. There are 6 emotions which are treated as universal emotions: anger, happiness, sadness, disgust, surprise and fear. This paper projects different emotion recognition systems which aim at enhancing the Human-Machine interaction. The techniques and systems used in emotion detection may vary depending on the features inspected. This paper explores them in a descriptive and comparative manner. Further the various applications that adopt these systems to reduce the difficulties in implementing the models in real-time are contemplated. Also, A multimodal system with both speech and facial features is proposed for emotion recognition through which it is possible to obtain an enhanced accuracy compare with the existing systems.

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“…The pixels in this block are threshold by its center pixel value, multiplied by powers of two and then summed to obtain a label for the center value. As the neighborhood consists of 8 pixels, a total of 2 8 = 256 different labels can be obtained depending on the relative gray values of the center and the pixels in the neighborhood. LBP code of each pixel in the image computed as follows:…”

Section: ) Face Boundary Detectionmentioning

confidence: 99%

Effectual Approach for Facial Expression Recognition System

Mahto¹,

Yadav²

2015

International Journal of Advanced Research in Computer and Comm

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Automatic facial expression is an interesting and challenging problem, and impacts important applications in many areas such as human-computer interaction and data-driven animation. Facial expression recognition is the process performed by computers which consist of detect the face in the image, and pre-process the face regions, extracting facial expression features from image by analysing the change in the appearance of facial features and then classifying this information into facial expression categories like fear, happy sad etc. In this research work, an Automatic Facial Expressions Recognition System is presented that recognizes five principal expressions that are Happy, Sad, Neutral, Anger and Disgust. The system uses an efficient approach for the recognition of those expressions on the basis of some extracted features. The whole system is implemented on the dataset of 150 images of frontal facial expressions of happy, sad, neutral, anger and disgust by using MATLAB. The images are collected from the Karolinska Directed Emotional Faces (KDEF) database. We empirically evaluate the facial representation based on local binary pattern (LBP) features. Then recognition performed by KNN classifier with LBP features. The result obtained after implementation is very good.

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A Classifier Model based on the Features Quantitative Analysis for Facial Expression Recognition

Cited by 6 publications

References 18 publications

Hyper Parameter Optimization using Genetic Algorithm on Machine Learning Methods for Online News Popularity Prediction

Hyper Parameter Optimization using Genetic Algorithm on Machine Learning Methods for Online News Popularity Prediction

Multimodal Approach to Emotion Recognition for Enhancing Human Machine Interaction - A Survey

Effectual Approach for Facial Expression Recognition System

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