Study of Spontaneous and Acted Learn-Related Emotions Through Facial Expressions and Galvanic Skin Response

Mitre-Ortiz, Andres; Mitre‐Hernandez, Hugo

doi:10.13053/rcs-148-5-11

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Emotions , have been widely studied by many disciplines in terms of valence and arousal [ 58 ], since affect recognition techniques could lead to various applications, such as, facial expression data sets [ 59 – 61 ], Facial Expression Recognition (FER) [ 62 – 64 ], physiological signals [ 65 – 67 ], sentiment analysis [ 68 , 69 ], speech emotion recognition [ 70 , 71 ], and health applications.…”

Section: Related Workmentioning

confidence: 99%

Developing a model to evaluate and improve user experience with hand motions in virtual reality environments

Mitre-Ortiz¹,

Muñoz-Arteaga

Cardona-Reyes

2022

Univ Access Inf Soc

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In video games, the evaluation of the user experience (UX) mainly refers to two main groups of aspects, those that refer to the player that is mainly oriented to make the player feel good while playing and those that refer to the video game that is oriented to make the video game easy to understand and play. The aspects considered that are related to the player are engagement, enjoyment, and flow; the aspects related to video game, usability, and dependability. Virtual reality environments today have changed the paradigm in various fields of application, such as health, education, entertainment, among others. Therefore, it is important to observe the effects of handedness with hand movements in virtual reality environments. This work proposes a model to evaluate and improve the user experience considering player and video game aspects, taking into account handedness with hand movements in virtual reality environments. Player and video game aspects can be added to evaluations of the effect of handedness, especially in virtual reality environments, in order to know the user’s behavior in terms of skill, performance, and accuracy, among other features by using a particular hand to perform specific tasks. Next, a case study is presented with two groups of users using a virtual reality environment to perform several user tasks considering the dominant and non-dominant hand. By evaluating the user tasks it is possible to know the levels of engagement, enjoyment, motivation, and usability in a virtual reality environment. Finally, an analysis of results is presented in which several improvements of UX are presented.

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Section: Related Workmentioning

confidence: 99%

Developing a model to evaluate and improve user experience with hand motions in virtual reality environments

Mitre-Ortiz¹,

Muñoz-Arteaga

Cardona-Reyes

2022

Univ Access Inf Soc

Self Cite

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The Application of a Hybrid Transfer Algorithm Based on a Convolutional Neural Network Model and an Improved Convolution Restricted Boltzmann Machine Model in Facial Expression Recognition

Wang

Song

et al. 2019

IEEE Access

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As an important part of emotion research, facial expression recognition is a necessary condition for intelligent interaction between human and machine, which has an important research significance and a potential commercial value. Convolutional neural network (CNN) is an effective method to recognize facial emotions, which can perform feature extraction and classification simultaneously, and can automatically discover multiple levels of representations in data. Due to the fact that there are millions of parameters involved in training the convolutional neural network model, and there is a large demand for marked samples, transfer learning is often used to fine-tune the pre-trained model for a small target sample set. However, there are often some content differences between data sets during deep transfer learning, which will affect the recognition ability of feature extraction. In order to improve the facial expression recognition ability in transfer learning, a hybrid transfer learning model based on an improved convolution restricted boltzmann machine (CRBM) model and a CNN model is proposed in this paper. This method is fused by two learning abilities of these two models. When the pre-trained CNN model is transferred to a small target set, the CRBM is used to replace the full connection layer in the CNN model, and the CRBM layer and the sofmax layer will be retrained on the target set. The added CRBM layer can not only fully connects all feature maps, but can also learn about the unique statistical characteristics about the target set, which eliminates the influence of content differences between data sets, and extracts higher-order statistical features of facial expression images from the target set. The proposed method is evaluated based on four publicly available facial expression databases: JAFFE, FER2013, SFEW and RAF-DB. The new method can achieve better performance than most stateof-the-art methods, and it can effectively prevent the negative influence of transfer learning features between different data sets.INDEX TERMS Facial expression recognition, convolutional neural network, convolution restricted Boltzmann machine, transfer learning.

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Study of Spontaneous and Acted Learn-Related Emotions Through Facial Expressions and Galvanic Skin Response

Cited by 2 publications

References 22 publications

Developing a model to evaluate and improve user experience with hand motions in virtual reality environments

Developing a model to evaluate and improve user experience with hand motions in virtual reality environments

The Application of a Hybrid Transfer Algorithm Based on a Convolutional Neural Network Model and an Improved Convolution Restricted Boltzmann Machine Model in Facial Expression Recognition

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