A smile/laughter recognition mechanism for smile-based life logging

Fukumoto, Kurara; Terada, Tsutomu; Tsukamoto, Masahiko

doi:10.1145/2459236.2459273

Cited by 30 publications

(11 citation statements)

References 10 publications

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“…However, techniques based on event fusion narrow the gap compared to uni-modal classification. The smile and laugh detector by Fukumoto et al [11] obtained recognition rates of 89.2 %. However, they had people watch videos of ten minutes only while we investigated social interactions over several hours in a mobile setting.…”

Section: Discussionmentioning

confidence: 99%

“…Typically, data conveying behavior, such as acceleration, skin conductance or voice, is obtained from mobile phones or wrist sensors and uploaded to a server for conducting statistical analysis. Typical applications include life logging systems, such as the smile and laughter detector presented by Fukumoto et al [11]. Also a number of health care systems follow this approach.…”

Section: Related Workmentioning

confidence: 99%

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MobileSSI: asynchronous fusion for social signal interpretation in the wild

Flutura

Wagner

Lingenfelser

et al. 2016

Proceedings of the 18th ACM International Conference on Multimodal Interaction

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Over the last years, mobile devices have become an integral part of people's everyday life. At the same time, they provide more and more computational power and memory capacity to perform complex calculations that formerly could only be accomplished with bulky desktop machines. These capabilities combined with the willingness of people to permanently carry them around open up completely new perspectives to the area of Social Signal Processing. To allow for an immediate analysis and interaction, real-time assessment is necessary. To exploit the benefits of multiple sensors, fusion algorithms are required that are able to cope with data loss in asynchronous data streams. In this paper we present MobileSSI, a port of the Social Signal Interpretation (SSI) framework to Android and embedded Linux platforms. We will test to what extent it is possible to run sophisticated synchronization and fusion mechanisms in an everyday mobile setting and compare the results with similar tasks in a laboratory environment. CCS Concepts •Human-centered computing → Open source software; Ubiquitous and mobile computing systems and tools;

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

MobileSSI: asynchronous fusion for social signal interpretation in the wild

Flutura

Wagner

Lingenfelser

et al. 2016

Proceedings of the 18th ACM International Conference on Multimodal Interaction

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“…Researchers have explored wearable interfaces for facial expression recognition [2]. Fukumoto et al [8] proposed a smile or laughter recognition method by using photo interrupters. Masai et al [19] proposed smart eye-wear with embedded photo reflective sensors for facial expression recognition.…”

Section: Facial Expression Recognition Interfacementioning

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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“…For instance, a microphone could be used for the speech and turn-related features and a head worn sensor, for example attached to a glass frame (or HMD), would allow the analysis of facial expressions [14].…”

Section: Information-sensitive Conversationmentioning

confidence: 99%

Augmenting Social Interactions

Damian

Tan

Baur

et al. 2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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Figure 1. Concept of the Logue system: A user wearing an HMD while giving a public speech (left). Using various sensors and social signal processing techniques (not illustrated), the user receives realtime feedback on his own behaviour superimposed on field of view (right). ABSTRACTNonverbal and unconscious behaviour is an important component of daily human-human interaction. This is especially true in situations such as public speaking, job interviews or information sensitive conversations, where researchers have shown that an increased awareness of one's behaviour can improve the outcome of the interaction. With wearable technology, such as Google Glass, we now have the opportunity to augment social interactions and provide realtime feedback on one's behaviour in an unobtrusive way. In this paper we present Logue, a system that provides realtime feedback on the presenters' openness, body energy and speech rate during public speaking. The system analyses the user's nonverbal behaviour using social signal processing techniques and gives visual feedback on a head-mounted display. We conducted two user studies with a staged and a real presentation scenario which yielded that Logue's feedback was perceived helpful and had a positive impact on the speaker's performance.

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A smile/laughter recognition mechanism for smile-based life logging

Cited by 30 publications

References 10 publications

MobileSSI: asynchronous fusion for social signal interpretation in the wild

MobileSSI: asynchronous fusion for social signal interpretation in the wild

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

Augmenting Social Interactions

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