Facial Expression Recognition with 3D Deformable Models

Mayer, Christoph; Wimmer, Matthias; Eggers, Martin; Radig, Bernd

doi:10.1109/achi.2009.33

Cited by 6 publications

(5 citation statements)

References 25 publications

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“…In previous works by the same authors [ 43 ], a sequence of RGB images was used for recognizing a set of basic emotions from facial expressions using Gabor filtering (a method that had several limitations based on the distance to the sensor). Others similar works are evaluated, which use the Cohn-Kanade Facial Expression Database and the mesh model Candide-3, but with different classification systems: Bayesian network [ 11 ] and model tree [ 10 ]. In order to perform a comparative study, four different aspects are taken into account: accuracy, the number of human emotions recognized by the algorithms, methods and classification systems.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…The use of classifiers, such as the Bayesian approach, represents a common solution in the literature for emotion recognition systems from facial expressions. However, there are many alternatives to this type of classifier, such as support vector machine (SVM) [ 9 ], model tree [ 10 ], binary decision tree [ 11 ] and neural networks [ 12 ], among others. Dynamic Bayesian networks are an interesting solution for emotion recognition from dynamic images.…”

Section: Introductionmentioning

confidence: 99%

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Muecas: A Multi-Sensor Robotic Head for Affective Human Robot Interaction and Imitation

Cid

Moreno

Bustos

et al. 2014

Sensors

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This paper presents a multi-sensor humanoid robotic head for human robot interaction. The design of the robotic head, Muecas, is based on ongoing research on the mechanisms of perception and imitation of human expressions and emotions. These mechanisms allow direct interaction between the robot and its human companion through the different natural language modalities: speech, body language and facial expressions. The robotic head has 12 degrees of freedom, in a human-like configuration, including eyes, eyebrows, mouth and neck, and has been designed and built entirely by IADeX (Engineering, Automation and Design of Extremadura) and RoboLab. A detailed description of its kinematics is provided along with the design of the most complex controllers. Muecas can be directly controlled by FACS (Facial Action Coding System), the de facto standard for facial expression recognition and synthesis. This feature facilitates its use by third party platforms and encourages the development of imitation and of goal-based systems. Imitation systems learn from the user, while goal-based ones use planning techniques to drive the user towards a final desired state. To show the flexibility and reliability of the robotic head, the paper presents a software architecture that is able to detect, recognize, classify and generate facial expressions in real time using FACS. This system has been implemented using the robotics framework, RoboComp, which provides hardware-independent access to the sensors in the head. Finally, the paper presents experimental results showing the real-time functioning of the whole system, including recognition and imitation of human facial expressions.

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Section: Experiments Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Muecas: A Multi-Sensor Robotic Head for Affective Human Robot Interaction and Imitation

Cid

Moreno

Bustos

et al. 2014

Sensors

View full text Add to dashboard Cite

show abstract

“…Before implicitly adapting to the mood of the user, the emotional state of the user has to be determined and mapped to the continuous PAD space. Ideally, this can be achieved by emotion recognition modules [34,60], but at least according to an explicit statement in the course of the social subdialog introduced above, and/or in combination with an initial self-assessment of the user on the PAD dimensions. When this is achieved, the robot shifts its base-PAD values for emotional expressions towards the mood of the user as a new starting point for potential emotional variations, e.g.…”

Section: Implicit Emotional Adaptionmentioning

confidence: 99%

Increasing Helpfulness towards a Robot by Emotional Adaption to the User

Kühnlenz

Sosnowski

Buß

et al. 2013

Int J of Soc Robotics

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This article describes an emotional adaption approach to proactively trigger increased helpfulness towards a robot in task-related human-robot interaction (HRI). Based on social-psychological predictions of human behavior, the approach aims at inducing empathy, paired with a feeling of similarity in human users towards the robot. This is achieved by two differently expressed emotional control variables: by an explicit statement of similarity before task-related interaction, and implicitly expressed by adapting the emotional state of the robot to the mood of the human user, such that the current values of the human mood in the dimensions of pleasure, arousal, and dominance (PAD) are matched. The thereby shifted emotional state of the robot serves as a basis for the generation of task-driven emotional facial-and verbal expressions, employed to induce and sustain high empathy towards the robot throughout the interaction. The approach is evaluated in a user study utilizing an expressive robot head. The effectiveness of the approach is confirmed by significant experimental results. An analysis of the individual components of the approach reveals significant effects of explicit emotional adaption on helpfulness, as well as on the HRI-key concepts anthropomorphism and animacy.

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“…The block size is chosen by trading off between efficiency and accuracy. In this procedure, each triangle is weighted Mayer et al (2009) 87.1% TAN Cohen et al (2003) 83.3% LBP + SVM Shan et al (2009) 92.6% IEBM Asthana et al (2009) 92.9% Fixed Jacobian Asthana et al (2009) 89.6% STMF + DT 93.2% Table 6. Comparison of gender classification in comparison to different approaches in Hadid & Pietikaeinen (2009) equally toward the feature calculation and face edges are not destroyed but rather provide the detailed texture information that might be lost during conventional image warping approach.…”

Section: Pose Invariant Face Recognitionmentioning

confidence: 99%