Feature Acquisition From Facial Expression Image Using Convolutional Neural Networks

Nishime, Taiki; Endo, Satoshi; Yamada, Kôji; Toma, Naruaki; Akamine, Yumiko

doi:10.2991/jrnal.2016.3.1.3

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…As this miniature sensor has small dimensions of 2.7 x 3.2 x 1.4 mm (W x L x H), 40 units are densely arranged at sampling points on the face capture mask, as shown in Figure 5. Following the standard practice of face recognition [24,25,33], we picked eyebrows, cheeks and around eyes and mouth as the sampling points of photo reflective sensors. We ensure that the sensors do not come into contact with the eyes, nose and mouth of the mask wearer.…”

Section: Facial Expression Identification Methodsmentioning

confidence: 99%

Digital Full-Face Mask Display with Expression Recognition using Embedded Photo Reflective Sensor Arrays

Takegawa

Tokuda²,

Umezawa

et al. 2020

2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)

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This paper presents a thin digital full-face mask display that can reflect an entire facial expression of a user onto an avatar to support augmented face-to-face communication in real environments. Although camera-based facial expression recognition technology has enabled people to augment their faces with avatars, application was limited to face-to-face communication in virtual environments. To enable digital facial augmentation with an avatar in a real space, we propose a digital face mask display system that integrates a lightweight flexible display with a thin facial expression recognition system. The thin wearable facial expression recognition system was implemented with photo reflective sensor arrays which can measure facial expressions at 40 feature points distributed across an entire face. We investigated a ten-class facial expression identification model based on an SVM training algorithm. The trained model achieved an average accuracy of 79% when identifying the facial expressions of multiple users. User experiments indicated that the proposed thin digital full-face mask display allows the wearer to control the facial expression of the avatar with a fast response rate and create a positive sense of self-agency and self-ownership toward the augmented avatar face.

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Section: Facial Expression Identification Methodsmentioning

confidence: 99%

Digital Full-Face Mask Display with Expression Recognition using Embedded Photo Reflective Sensor Arrays

Takegawa

Tokuda²,

Umezawa

et al. 2020

2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)

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“…CNN is good at processing images [26]. Traditional face recognition methods show poor results when facing complex scenes.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Improved Facial Expression Recognition Method Based on GAN

Jun-huan

2021

Scientific Programming

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Recognizing facial expressions accurately and effectively is of great significance to medical and other fields. Aiming at problem of low accuracy of face recognition in traditional methods, an improved facial expression recognition method is proposed. The proposed method conducts continuous confrontation training between the discriminator structure and the generator structure of the generative adversarial networks (GANs) to ensure enhanced extraction of image features of detected data set. Then, the high-accuracy recognition of facial expressions is realized. To reduce the amount of calculation, GAN generator is improved based on idea of residual network. The image is first reduced in dimension and then processed to ensure the high accuracy of the recognition method and improve real-time performance. Experimental part of the thesis uses JAFEE dataset, CK + dataset, and FER2013 dataset for simulation verification. The proposed recognition method shows obvious advantages in data sets of different sizes. The average recognition accuracy rates are 96.6%, 95.6%, and 72.8%, respectively. It proves that the method proposed has a generalization ability.

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“…In this study, we focus on research on human facial expression recognition [11] to provide autonomous learning support through teacher-type robots. In particular, methods based on deep learning have been widely used in research on facial expressions, and have shown high performance in image recognition and image classification Convolutional neural networks (hereafter, this is called CNN) have been proposed [12]. However, conventional research on facial expression recognition has focused only on the seven basic emotions of anger, disgust, fear, happy, sad, surprise, and neutral (hereafter referred to as the seven basic emotions), and has not focused on perplexion.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Impression Effects of a Teacher-Type Robot Equipped a Perplexion Estimation Method on College Students

Okawa¹,

Jimenez²,

Akizuki³

et al. 2023

Adv. sci. technol. eng. syst. j.

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In recent years, the adoption of ICT education has increased in educational settings. Research and development of educational support robots have garnered considerable interest as a promising approach to inspire and engage students. Conventional robots provide learning support through button operations by the learners. However, the frequent need for button operation to request support may lead to a tedious impression on the learner and lower the efficiency of the learning process. Therefore, in this study, we developed a Perplexion Estimation Method that estimates the learner's state of perplexity by analyzing their facial expressions and provides autonomous learning support. We verified the impact of a teacher-type robot (referred to as the proposed robot) that autonomously provides learning support by estimating the learners' perplexity states in joint learning with university students. The results of a subject experiment showed that the impression of the proposed robot was not different from that of the conventional robot. However, the proposed robot demonstrated the ability to provide optimal support timing compared to the conventional robot. Based on these results, it is expected that the utilization of the perplexion estimation method with teacher-type robots can create a learning environment similar to human-to-human interaction.

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Feature Acquisition From Facial Expression Image Using Convolutional Neural Networks

Cited by 4 publications

References 2 publications

Digital Full-Face Mask Display with Expression Recognition using Embedded Photo Reflective Sensor Arrays

Digital Full-Face Mask Display with Expression Recognition using Embedded Photo Reflective Sensor Arrays

Improved Facial Expression Recognition Method Based on GAN

Investigating the Impression Effects of a Teacher-Type Robot Equipped a Perplexion Estimation Method on College Students

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