Eyeball Movement Detection Using Sector Division Approach and Extreme Learning Machine

Bachtiar, Fitra A.; Pangestu, Gusti; Pradana, Fajar; Arwani, Issa; Syauqy, Dahnial

doi:10.1109/isitia52817.2021.9502211

Cited by 1 publication

(1 citation statement)

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“…Convolutional neural network (CNN) which is used as a food image classification [15], [16] and deep belief network (DBN) for recognition of emotions on human faces [17]. Lastly, extreme learning machine (ELM) which is used in real-world classification problems, including breast cancer, authentication on banknotes, [18], detecting eye fixation point [19], and so on. Those previous study show that the NN architectural model is able to perform various classification processes with good results.…”

Section: Introductionmentioning

confidence: 99%

Feature extraction comparison for facial expression recognition using adaptive extreme learning machine

Wafi

Bachtiar

Utaminingrum

2023

IJECE

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Facial expression recognition is an important part in the field of affective computing. Automatic analysis of human facial expression is a challenging problem with many applications. Most of the existing automated systems for facial expression analysis attempt to recognize a few prototypes emotional expressions such as anger, contempt, disgust, fear, happiness, neutral, sadness, and surprise. This paper aims to compare feature extraction methods that are used to detect human facial expression. The study compares the gray level co-occurrence matrix, local binary pattern, and facial landmark (FL) with two types of facial expression datasets, namely Japanese female facial expression (JFFE), and extended Cohn-Kanade (CK+). In addition, we also propose an enhancement of extreme learning machine (ELM) method that can adaptively select best number of hidden neurons adaptive ELM (aELM) to reach its maximum performance. The result from this paper is our proposed method can slightly improve the performance of basic ELM method using some feature extractions mentioned before. Our proposed method can obtain maximum mean accuracy score of 88.07% on CK+ dataset, and 83.12% on JFFE dataset with FL feature extraction.

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