Human motions and emotions recognition inspired by LMA qualities

Ajili, Insaf; Mallem, Malik; Didier, Jean‐Yves

doi:10.1007/s00371-018-01619-w

Cited by 32 publications

(16 citation statements)

References 42 publications

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“…In order to verify the influence of the number of neurons in the LSTM layer on the recognition results, this article verifies the accuracy of the number of different neurons in 6,12,18,24,30,36, 42 on the test set. As shown in Figure 9, the accuracy is highest when the number of neurons is 36.…”

Section: Lstm Model Trainingmentioning

confidence: 83%

“…Laban [11] proposed a method to describe dance movements, namely Laban Movement Analysis (LMA), in order to analyze dance movements scientifically. At present, the LMA method is widely used in emotion recognition as a description method of limb movements, for example, Ajili I [12] proposed a new human action description vector based on Laban action analysis method to recognize human expressions and actions on video images. [13] based on LMA, the relationship between human movement and emotion was studied, and the results showed that there was a good correlation between LMA characteristics and emotion.…”

Section: Introductionmentioning

confidence: 99%

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Dance Emotion Recognition Based on Laban Motion Analysis Using Convolutional Neural Network and Long Short-Term Memory

Wang

Cao

et al. 2020

IEEE Access

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Dance emotion recognition technology is of great significance for the digitalization, virtual performance, inheritance and protection of folk dance. Based on the mechanism that emotion expression in dance performance can be fully expressed through the strength and rhythm of dance movements, a novel dance emotion expression method is proposed to train hybrid deep learning neural network, to effectively identify the seven basic dance emotions of fear, anger, boredom, excitement, joy, relaxation and sadness. First, in order to fully express the emotions contained in the dance movements, this paper defines a dance emotion expression method through Laban Movement Analysis (LMA) method, which includes the characteristic parameters of the three aspects of body structure, spatial orientation and force effect, and converts the original dance movement data into three characteristic expression parameters to obtain dance emotion data. Then, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) hybrid neural network models are used to test and train dance emotion data. Finally, in order to verify the applicability of the CNN-LSTM model, decision tree, random forest, CNN and LSTM are established and compared for accuracy. The results show that it is feasible to identify dance emotion from the perspective of dance movement, and the CNN-LSTM model is of high accuracy.

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Section: Lstm Model Trainingmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Dance Emotion Recognition Based on Laban Motion Analysis Using Convolutional Neural Network and Long Short-Term Memory

Wang

Cao

et al. 2020

IEEE Access

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“…People will involuntarily undergo some posture changes in different environmental states and moods, and judge human emotions based on physical information such as the time and frequency of these posture changes, according to gesture-based emotion recognition. Ajili et al (2019) used human movement analysis to identify motion, then evaluated and evaluated the emotions expressed by human motion posture. However, the single use of human gestures for emotion recognition has certain limitations, because many gestures do not have emotional significance or the same gestures have different emotional meanings in different background environments, so human gestures are usually different from others.…”

Section: Related Workmentioning

confidence: 99%

Facial Expression Emotion Recognition Model Integrating Philosophy and Machine Learning Theory

Song

2021

Front. Psychol.

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Facial expression emotion recognition is an intuitive reflection of a person’s mental state, which contains rich emotional information, and is one of the most important forms of interpersonal communication. It can be used in various fields, including psychology. As a celebrity in ancient China, Zeng Guofan’s wisdom involves facial emotion recognition techniques. His book Bing Jian summarizes eight methods on how to identify people, especially how to choose the right one, which means “look at the eyes and nose for evil and righteousness, the lips for truth and falsehood; the temperament for success and fame, the spirit for wealth and fortune; the fingers and claws for ideas, the hamstrings for setback; if you want to know his consecution, you can focus on what he has said.” It is said that a person’s personality, mind, goodness, and badness can be showed by his face. However, due to the complexity and variability of human facial expression emotion features, traditional facial expression emotion recognition technology has the disadvantages of insufficient feature extraction and susceptibility to external environmental influences. Therefore, this article proposes a novel feature fusion dual-channel expression recognition algorithm based on machine learning theory and philosophical thinking. Specifically, the feature extracted using convolutional neural network (CNN) ignores the problem of subtle changes in facial expressions. The first path of the proposed algorithm takes the Gabor feature of the ROI area as input. In order to make full use of the detailed features of the active facial expression emotion area, first segment the active facial expression emotion area from the original face image, and use the Gabor transform to extract the emotion features of the area. Focus on the detailed description of the local area. The second path proposes an efficient channel attention network based on depth separable convolution to improve linear bottleneck structure, reduce network complexity, and prevent overfitting by designing an efficient attention module that combines the depth of the feature map with spatial information. It focuses more on extracting important features, improves emotion recognition accuracy, and outperforms the competition on the FER2013 dataset.

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“…The performance of artificial agents would improve in human–agent interaction if they had good emotion recognition ability with appropriate conduct [ 1 , 2 , 3 ]. Centered on this topic, much research has examined facial expression recognition [ 4 , 5 ], speech emotion recognition [ 6 , 7 ], motion emotion recognition [ 8 , 9 ], and text emotion recognition [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Modality Emotion Recognition Model with GAT-Based Multi-Head Inter-Modality Attention

Liu

Ishi

et al. 2020

Sensors

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Emotion recognition has been gaining attention in recent years due to its applications on artificial agents. To achieve a good performance with this task, much research has been conducted on the multi-modality emotion recognition model for leveraging the different strengths of each modality. However, a research question remains: what exactly is the most appropriate way to fuse the information from different modalities? In this paper, we proposed audio sample augmentation and an emotion-oriented encoder-decoder to improve the performance of emotion recognition and discussed an inter-modality, decision-level fusion method based on a graph attention network (GAT). Compared to the baseline, our model improved the weighted average F1-scores from 64.18 to 68.31% and the weighted average accuracy from 65.25 to 69.88%.

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Human motions and emotions recognition inspired by LMA qualities

Cited by 32 publications

References 42 publications

Dance Emotion Recognition Based on Laban Motion Analysis Using Convolutional Neural Network and Long Short-Term Memory

Dance Emotion Recognition Based on Laban Motion Analysis Using Convolutional Neural Network and Long Short-Term Memory

Facial Expression Emotion Recognition Model Integrating Philosophy and Machine Learning Theory

Multi-Modality Emotion Recognition Model with GAT-Based Multi-Head Inter-Modality Attention

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