Computer Recognition of Facial Expressions of Emotion

Piątkowska, Ewa; Martyna, Jerzy

doi:10.1007/978-3-642-31537-4_32

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…Darwin conducted a research in 1872 on 'The Expression of Emotions in Man and Animals ' Darwin, C. (1872). In another work, Ekman and Friesen in 1978 reported on 'The Facial Action Coding System: A Technique for The Measurement of Facial Movement' with the San Francisco: Consulting Psychologists Press (Piatkowska, & Martyna, 2012;Ekman, & Friesen, 1978). These opened up a bright insight for researchers to go deeply into optimising and effective discrimination of FER using different techniques which include: Principal component analysis (PCA); Speeded Up Robust Features (SURF); Local binary pattern (LBP); Gray-Level Cooccurrence Matrix (GLCM) and also the group sparse coding (GSC) (Bengio et al, 2009).…”

Section: Can Be Cited Asmentioning

confidence: 99%

“…In another similar report, Ekman & Friesen (1976) conducted many researches on the work of Darwin to give a better insight on FER. Recent reports on FER were computer-based research (Piatkowska & Martyna, 2012) where they proposed using a support vector machine (SVM) system that will detect and classify spontaneous emotions. They encoded the face texture with Local Binary Patterns (LBP) using face detection algorithm to create face representation.…”

Section: Related Workmentioning

confidence: 99%

“…These researches are also necessary due to the wide variety of human computer interaction applications that FER is required, these include, but not limited to pain detection in medical treatment, driver's mood in driving software, student's reaction in an online class, customer's mood in a business environment etc. (Piatkowska & Martyna, 2012). In the educational sector, many factors like the virtual learning environment, the curriculum content and the teacher's approach to students determine the success in simulating interaction and student's mood in the learning process (Yang, et al 2017;Dass, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Facial Emotion Recognition with Sparse Coding Descriptor

et al. 2021

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With the Corona Virus Disease 2019 (COVID-19) global pandemic ravaging the world, all sectors of life were affected including education. This led to many schools taking distance learning through the use of computer as a safer option. Facial emotion means a lot to teacher’s assessment of his performance and relation to his students. Researchers has been working on improving the face monitoring and human machine interface. In this paper we presented different types of face recognition methods which include: Principal component analysis (PCA); Speeded Up Robust Features (SURF); Local binary pattern (LBP); Gray-Level Co-occurrence Matrix (GLCM) and also the group sparse coding (GSC) and come up with the fusion of LBP, PCA, SURF GLCM with GSC. Linear Kernel Support Vector Machine (LSVM) Classifier out-performed Polynomial, RBF and Sigmoid kernels SVM in the emotion classification. Results obtained from experiments indicated that, the new fusion method is capable of differentiating different types of face emotions with higher accuracy compare with the state-of-the-art methods currently available.Keywords— Face Recognition, Principal Component Analysis, Speeded Up Robust Features, Grey Level Co-Occurrence Matrix

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Section: Can Be Cited Asmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Facial Emotion Recognition with Sparse Coding Descriptor

et al. 2021

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“…One of the most important application of pattern classification is Facial Expression Recognition [14]. In this field, graph theory [5] provides a suitable mathematical model of a human face.…”

Section: Introductionmentioning

confidence: 99%

Facial Expression Recognition on a Quantum Computer

Mengoni¹,

Incudini²,

Pierro³

2021

Preprint

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We address the problem of facial expression recognition and show a possible solution using a quantum machine learning approach. In order to define an efficient classifier for a given dataset, our approach substantially exploits quantum interference. By representing face expressions via graphs, we define a classifier as a quantum circuit that manipulates the graphs adjacency matrices encoded into the amplitudes of some appropriately defined quantum states.We discuss the accuracy of the quantum classifier evaluated on the quantum simulator available on the IBM Quantum Experience cloud platform, and compare it with the accuracy of one of the best classical classifier.

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Music Recommender Based on the Facial Emotion of the User Identified Using YOLOV8

Nair,

Kanojia

2024

Intelligent Systems Design and Applications

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Computer Recognition of Facial Expressions of Emotion

Cited by 9 publications

References 9 publications

Facial Emotion Recognition with Sparse Coding Descriptor

Facial Emotion Recognition with Sparse Coding Descriptor

Facial Expression Recognition on a Quantum Computer

Music Recommender Based on the Facial Emotion of the User Identified Using YOLOV8

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