Facial Expression Recognition for Human-Robot Interaction – A Prototype

Wimmer, Matthias; MacDonald, Bruce A.; Jayamuni, Dinuka; Yadav, Arpit

doi:10.1007/978-3-540-78157-8_11

Cited by 22 publications

(23 citation statements)

References 24 publications

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“…A number of HRI studies have incorporated the use of affect classification using categorical emotional models for facial expressions [57][58][59][60], body language [61][62][63][64], voice [7,19,[65][66][67][68][69], physiological signals [70][71][72], and multi-modal systems [73,74]. These models allow robots to interpret affective states in a similar manner as humans [75].…”

Section: Affect Models Used In Hrimentioning

confidence: 99%

A Survey of Autonomous Human Affect Detection Methods for Social Robots Engaged in Natural HRI

McColl

Hong

Hatakeyama

et al. 2015

J Intell Robot Syst

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In Human-Robot Interactions (HRI), robots should be socially intelligent. They should be able to respond appropriately to human affective and social cues in order to effectively engage in bi-directional communications. Social intelligence would allow a robot to relate to, understand, and interact and share information with people in real-world humancentered environments. This survey paper presents an encompassing review of existing automated affect recognition and classification systems for social robots engaged in various HRI settings. Human-affect detection from facial expressions, body language, voice, and physiological signals are investigated, as well as from a combination of the aforementioned modes. The automated systems are described by their corresponding robotic and HRI applications, the sensors they employ, and the feature detection techniques and affect classification strategies utilized. This paper also discusses pertinent future research directions for promoting the development of socially intelligent robots capable of recognizing, classifying and responding to human affective states during real-time HRI.

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Section: Affect Models Used In Hrimentioning

confidence: 99%

A Survey of Autonomous Human Affect Detection Methods for Social Robots Engaged in Natural HRI

McColl

Hong

Hatakeyama

et al. 2015

J Intell Robot Syst

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show abstract

“…For example, it could provide the user's intentions and feelings to a machine or robot to enable it to respond more appropriately during the service (Essa & Pentland, 1997). In the application of robot-assisted learning whereby a robot is used to teach the user by explaining the content of the lesson and question the user afterwards, understanding the human emotion will enable the robot to progress from one lesson to the next when the user is ready (Wimmer et al, 2008). Video conferencing, tele-presence and tele-teaching require transmission of a large amount of data, and data compression is often needed in order to reduce the storage and bandwidth requirements.…”

Section: Applicationsmentioning

confidence: 99%

“…The extracted features should represent different types of facial expressions in a way which is not significantly affected by age, gender, or ethnic origin of the subject. The classification method must be capable to define appropriate rules in order to derive a specific type of facial expression from the facial features provided, even when the output from the preceding processing stages, such as facial data acquisition and facial feature extraction, is noisy or incomplete (Wimmer et al, 2008).…”

Section: Challengesmentioning

confidence: 99%

Facial Expression Recognition

Matuszewski¹,

Quan²,

Shark³

2011

Biometrics - Unique and Diverse Applications in Nature, Science, and Technology

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“…However, almost all methods for HRI are developed for controlling robots, not for interacting with them. For natural communications between robots and humans, it is necessary for the robot to be able to respond according to the user's emotional state [1]. In general, the user may feel more satisfied with and friendly towards the robot when it responds in parallel with the user's emotion.…”

Section: Introductionmentioning

confidence: 99%

“…In general, the user may feel more satisfied with and friendly towards the robot when it responds in parallel with the user's emotion. Facial expression is one of the most important means of interaction in using emotions [1,2,3]. To recognize the facial expressions of the input face, it is important for the robot to get an accurate reading of the positions of facial feature points, such as the nose, mouse, eyes and eyebrows, since the locations of the facial feature points are largely varied according to the facial expression.…”

Section: Introductionmentioning

confidence: 99%

Emotional Interaction with a Robot Using Facial Expressions, Face Pose and Hand Gestures

Kang

2012

International Journal of Advanced Robotic Systems

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Facial expression is one of the major cues for emotional communications between humans and robots. In this paper, we present emotional human robot interaction techniques using facial expressions combined with an exploration of other useful concepts, such as face pose and hand gesture. For the efficient recognition of facial expressions, it is important to understand the positions of facial feature points. To do this, our technique estimates the 3D positions of each feature point by constructing 3D face models fitted on the user. To construct the 3D face models, we first construct an Active Appearance Model (AAM) for variations of the facial expression. Next, we estimate depth information at each feature point from frontal-and side-view images. By combining the estimated depth information with AAM, the 3D face model is fitted on the user according to the various 3D transformations of each feature point. Selfocclusions due to the 3D pose variation are also processed by the region weighting function on the normalized face at each frame. The recognized facial expressions -such as happiness, sadness, fear and anger -are used to change the colours of foreground and background objects in the robot displays, as well as other robot responses. The proposed method displays desirable results in viewing comics with the entertainment robots in our experiments.

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Facial Expression Recognition for Human-Robot Interaction – A Prototype

Cited by 22 publications

References 24 publications

A Survey of Autonomous Human Affect Detection Methods for Social Robots Engaged in Natural HRI

A Survey of Autonomous Human Affect Detection Methods for Social Robots Engaged in Natural HRI

Facial Expression Recognition

Emotional Interaction with a Robot Using Facial Expressions, Face Pose and Hand Gestures

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