Emotion recognition using sigma-pi neural network

Kulishova, Nonna

doi:10.1109/dsmp.2016.7583569

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…The MENFN's framework confirms a sufficiently higher learning rate as opposed to a scheme described in [30]. A plot for errors' change by epochs is shown in Fig.6; results of learning are demonstrated in Table 1.…”

Section: An Extended Neo-fuzzy Neuronmentioning

confidence: 76%

A Multidimensional Extended Neo-Fuzzy Neuron for Facial Expression Recognition

Hu¹,

Bodyanskiy²,

Kulishova³

et al. 2017

IJISA

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Abstract-An article introduces a modified architecture of the neo-fuzzy neuron, also known as a "multidimensional extended neo-fuzzy neuron" (MENFN), for the face recognition problems. This architecture is marked by enhanced approximating capabilities. A characteristic property of the MENFN is also its computational plainness in comparison with neuro-fuzzy systems and neural networks. These qualities of the proposed system make it effectual for solving the image recognition problems. An introduced MENFN's adaptive learning algorithm allows solving classification problems in a real-time fashion.

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Section: An Extended Neo-fuzzy Neuronmentioning

confidence: 76%

A Multidimensional Extended Neo-Fuzzy Neuron for Facial Expression Recognition

Hu¹,

Bodyanskiy²,

Kulishova³

et al. 2017

IJISA

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“…In [26] , the author used the steepest descent optimization method to train SP-ANN. The SP-ANN approach was applied in [27] to identify emotions. Then, these methods are based on the second derivative.…”

Section: Related Workmentioning

confidence: 99%

Learner Emotional Value Recognition Technology: An Artificial Neural Network Optimized by the Grey Wolf Algorithm

Chen

2023

IEEE Access

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In cross-cultural communication, multimedia animation is crucial in defining a nation's image and cultural form. It is a key vehicle for cultural diffusion and a tool for film and television to convey national culture and highlight regional culture. Animation's particular charm, position, and function in cultural dissemination are further highlighted by the special cinematic language used to portray emotions, and it also reflects the medium's unique significance in the rapidly evolving modern society. To better understand the emotions generated by various multimedia animations, in-depth research is needed. To investigate these issues, this article explores the use of the Sigma-pi artificial neural network (SP-ANN) algorithm based on the grey wolf optimization algorithm (GWOA) to identify emotional states. Compared with traditional Sigma π artificial neural network algorithms, a training process that does not require complex derivative calculations in derivative-based algorithms is performed. Sigma π networks can benefit from the proposed learning algorithms. This algorithm has high approximation accuracy and is particularly suitable for real-time approximation of nonlinear processes. The test results indicate that the proposed algorithm can work as expected.INDEX TERMS Multimedia animation; grey wolf optimization algorithm; sigma pi artificial neural network algorithms

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“…One of the promising areas for such intellectual interfaces' development is the approach that uses the recognition of people, their age, sex, state of health, emotional status on the real time video. This complex technical problem already finds its own solutions [1][2][3][4][5][6][7][8][9]. Frequently, these decisions use the machine learning and neuro-fuzzy approach.…”

Section: Introductionmentioning

confidence: 99%

Multilayer GMDH-neuro-fuzzy network based on extended neo-fuzzy neurons and its application in online facial expression recognition

Bodyanskiy

Zaychenko

Hamidov³

et al. 2020

SRIT

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Real-time image recognition is required in many important practical problems. Interaction with users in online mode requires flexibility and adaptability from applications. The Group Method of Data Handling (GMDH) allows changing the model structure and adjusting the system architecture to the characteristics of each task under consideration. Moreover, the approximating properties of neo-fuzzy neurons used as elements of the system provide the high recognition accuracy under conditions of short data samples. This paper proposes a multilayer GMDH-neurofuzzy network based on extended neo-fuzzy neurons. The learning algorithm has filtering and tracking properties, guarantees the required speed important for real-time applications. The effectiveness of the proposed system is confirmed for the human emotions recognition.

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Emotion recognition using sigma-pi neural network

Cited by 7 publications

References 5 publications

A Multidimensional Extended Neo-Fuzzy Neuron for Facial Expression Recognition

A Multidimensional Extended Neo-Fuzzy Neuron for Facial Expression Recognition

Learner Emotional Value Recognition Technology: An Artificial Neural Network Optimized by the Grey Wolf Algorithm

Multilayer GMDH-neuro-fuzzy network based on extended neo-fuzzy neurons and its application in online facial expression recognition

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